From 972cda5d520ef43bd25cfb65071aea5bd015d20c Mon Sep 17 00:00:00 2001 From: cjwood Date: Mon, 17 Dec 2018 16:00:58 -0500 Subject: [PATCH 1/2] move example jupyter notebooks to qiskit-tutorials --- README.md | 2 +- examples/aer_provider.ipynb | 502 ------------------ examples/custom_unitary_gates.ipynb | 450 ---------------- examples/device_noise_simulation.ipynb | 337 ------------ examples/images/qiskit-heading.gif | Bin 4834 -> 0 bytes examples/images/qiskit-heading.png | Bin 86967 -> 0 bytes examples/noise_examples.ipynb | 702 ------------------------- 7 files changed, 1 insertion(+), 1992 deletions(-) delete mode 100644 examples/aer_provider.ipynb delete mode 100644 examples/custom_unitary_gates.ipynb delete mode 100644 examples/device_noise_simulation.ipynb delete mode 100644 examples/images/qiskit-heading.gif delete mode 100644 examples/images/qiskit-heading.png delete mode 100644 examples/noise_examples.ipynb diff --git a/README.md b/README.md index 5ec50f0f4f..dca8bd8d82 100755 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ This is a working draft for the Qiskit-Aer simulator framework for Qiskit-Terra. * The **qiskit_aer** folder contains the Qiskit-Aer python module for use with Qiskit-Terra. * The **cmake** folder contains cmake scripts for building the simulator * The **src** folder contains the C++ source files for building the simulator. -* The **examples** folder contains example Jupyter notebooks. * The **contrib** folder for external contributions. * The **test** folder contains simulator unit tests. @@ -17,6 +16,7 @@ This is a working draft for the Qiskit-Aer simulator framework for Qiskit-Terra. There's a [contributing guide](https://github.ibm.com/IBMQuantum/qiskit-aer/blob/master/.github/CONTRIBUTING.rst) with more detailed information about the project. +For tutorials on using Qiskit Aer visit the [qiskit-tutorials](https://github.com/Qiskit/qiskit-tutorials/tree/master/qiskit/aer) Github repository. ## Installation diff --git a/examples/aer_provider.ipynb b/examples/aer_provider.ipynb deleted file mode 100644 index 7848b360cb..0000000000 --- a/examples/aer_provider.ipynb +++ /dev/null @@ -1,502 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Note: Trusted Notebook\" width=\"500 px\" align=\"left\">" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Qiskit Aer: Simulators\n", - "\n", - "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n", - "\n", - "**NOTE: THIS ONLY WORKS ON QISKIT-TERRA 0.7, QISKIT-AER 0.1**\n", - "\n", - "***\n", - "### Contributors\n", - "Christopher J. Wood$^{1}$\n", - "\n", - "1. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook shows how to import *Qiskit Aer* simulators backends and use them to execut ideal (noise free) Qiskit Terra circuits." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Import Qiskit\n", - "from qiskit_aer import Aer\n", - "from qiskit import execute, QuantumCircuit, QuantumRegister, ClassicalRegister\n", - "from qiskit.tools.visualization import plot_histogram, plot_state_city" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Qiskit Aer simulator backends\n", - "\n", - "Currently Qiskit Aer currently includes three high performance simulator backends:\n", - "* `QasmSimulator`: Allows ideal and noisy multi-shot execution of qiskit circuits and returns counts or memory\n", - "* `StatevectorSimulator`: Allows ideal single-shot execution of qiskit circuits and returns the final statevector of the simulator after application\n", - "* `UnitarySimulator`: Allows ideal single-shot execution of qiskit circuits and returns the final unitary matrix of the circuit itself. Note that the circuit cannot contain measure or reset operations for this backend\n", - "\n", - "These backends are found in the `Aer` provider with the names `qasm_simulstor`, `statevector_simulator` and `unitary_simulator` respectively" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit_aer import Aer\n", - "\n", - "# List Aer backends\n", - "Aer.backends()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The backends can also be directly imported from the `qiskit_aer.backends` without using the provider" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_aer.backends import QasmSimulator, StatevectorSimulator, UnitarySimulator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## QasmSimulator\n", - "\n", - "The `QasmSimulator` backend is designed to mimic an actual device. It executes a Qiskit `QuantumCircuit` and returns a count dictionary containing the final values of any classical registers in the circuit. The circuit may contain *gates*\n", - "*measure*, *reset*, *conditionals*, and other advanced simulator options that will be discussed in another notebook.\n", - "\n", - "### Simulating a quantum circuit\n", - "\n", - "The basic operation executes a quantum circuit and returns a counts dictionary of measurement outcomes. We consider executing a simple circuit that repares a 2-qubit Bell-state $|\\psi\\rangle = \\frac{1}{2}(|0,0\\rangle + |1,1 \\rangle)$ and measures both qubits." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct quantum circuit\n", - "qr = QuantumRegister(2, 'qr')\n", - "cr = ClassicalRegister(2, 'cr')\n", - "circ = QuantumCircuit(qr, cr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "circ.measure(qr, cr)\n", - "\n", - "# Select the QasmSimulator from the Aer provider\n", - "simulator = Aer.get_backend('qasm_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator).result()\n", - "counts = result.get_counts(circ)\n", - "plot_histogram(counts, title='Bell-State counts')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Returning measurements outcomes for each shot\n", - "\n", - "The `QasmSimulator` also supports returning a list of measurement outcomes for each individual shot. This is enabled by setting the key-word argument `memory=True` in the `compile` or `execute` function." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['00', '00', '00', '00', '11', '00', '00', '00', '00', '11']\n" - ] - } - ], - "source": [ - "# Construct quantum circuit\n", - "qr = QuantumRegister(2, 'qr')\n", - "cr = ClassicalRegister(2, 'cr')\n", - "circ = QuantumCircuit(qr, cr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "circ.measure(qr, cr)\n", - "\n", - "# Select the QasmSimulator from the Aer provider\n", - "simulator = Aer.get_backend('qasm_simulator')\n", - "\n", - "# Execute and get memory\n", - "result = execute(circ, simulator, shots=10, memory=True).result()\n", - "memory = result.get_memory(circ)\n", - "print(memory)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Starting simulation with a custom initial state\n", - "\n", - "The `QasmSimulator` allows setting a custom initial statevector for the simulation. This means that all experiments in a Qobj will be executed starting in a state $|\\psi\\rangle$ rather than the all zero state $|0,0,..0\\rangle$. The custom state may be set using the `backend_options` key-word arg for `execute`, or the Aer backend `run` method.\n", - "\n", - "**Note:**\n", - "* The initial statevector must be a valid quantum state $|\\langle\\psi|\\psi\\rangle|=1$. If not an exception will be raised. \n", - "* If a Qobj contains multiple circuits, the initial statevector must be the correct size fo *all* experiments in the Qobj, otherwise an exception will be raised.\n", - "\n", - "We now demonstate this functionality be executing an empty circuit, but setting the simulator to be initialized in the the final Bell-state of the previous example:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct an empty quantum circuit\n", - "qr = QuantumRegister(2)\n", - "cr = ClassicalRegister(2)\n", - "circ = QuantumCircuit(qr, cr)\n", - "circ.measure(qr, cr)\n", - "\n", - "# Set the initial state\n", - "opts = {\"initial_statevector\": np.array([1, 0, 0, 1] / np.sqrt(2))}\n", - "\n", - "# Select the QasmSimulator from the Aer provider\n", - "simulator = Aer.get_backend('qasm_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator, backend_options=opts).result()\n", - "counts = result.get_counts(circ)\n", - "plot_histogram(counts, title=\"Bell initial statevector\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## StatevectorSimulator\n", - "\n", - "\n", - "The `StatevectorSimulator` executes a single shot of a Qiskit `QuantumCircuit` and returns the final quantum statevector of the simulation. The circuit may contain *gates*, and also *measure*, *reset*, and *conditionals*.\n", - "\n", - "### Simulating a quantum circuit\n", - "\n", - "The basic operation executes a quantum circuit and returns a counts dictionary of measurement outcomes. We consider executing a simple circuit that repares a 2-qubit Bell-state $|\\psi\\rangle = \\frac{1}{2}(|0,0\\rangle + |1,1 \\rangle)$ and measures both qubits." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct quantum circuit without measure\n", - "qr = QuantumRegister(2, 'qr')\n", - "circ = QuantumCircuit(qr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "\n", - "# Select the StatevectorSimulator from the Aer provider\n", - "simulator = Aer.get_backend('statevector_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator).result()\n", - "statevector = result.get_statevector(circ)\n", - "plot_state_city(statevector, title='Bell state')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulating a quantum circuit with measurement\n", - "\n", - "Note that if a circuit contains *measure*, *reset*, the final statevector will be a conditional statevector *after* simulating wave-function collapse to the outcome of a measure or reset. For the Bell-state circuit this means the final statevector will be *either* $|0,0\\rangle$ *or* $|1, 1\\rangle$." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct quantum circuit with measure\n", - "qr = QuantumRegister(2, 'qr')\n", - "cr = ClassicalRegister(2, 'cr')\n", - "circ = QuantumCircuit(qr, cr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "circ.measure(qr, cr)\n", - "\n", - "# Select the StatevectorSimulator from the Aer provider\n", - "simulator = Aer.get_backend('statevector_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator).result()\n", - "statevector = result.get_statevector(circ)\n", - "plot_state_city(statevector, title='Bell state post-measurement')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Starting simulation with a custom initial state\n", - "\n", - "Like the `QasmSimulator`, the `StatevectorSimulator` also allows setting a custom initial statevector for the simulation. Here we run the previous initial statevector example on the `StatevectorSimulator` to initialize it to the Bell state and execute an empty (identity) circuit.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct an empty quantum circuit\n", - "qr = QuantumRegister(2)\n", - "circ = QuantumCircuit(qr)\n", - "circ.iden(qr)\n", - "\n", - "# Set the initial state\n", - "opts = {\"initial_statevector\": np.array([1, 0, 0, 1] / np.sqrt(2))}\n", - "\n", - "# Select the StatevectorSimulator from the Aer provider\n", - "simulator = Aer.get_backend('statevector_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator, backend_options=opts).result()\n", - "statevector = result.get_statevector(circ)\n", - "plot_state_city(statevector, title=\"Bell initial statevector\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Unitary Simulator\n", - "\n", - "The `UnitarySimulator` constructs the unitary matrix for a Qiskit `QuantumCircuit` by applying each gate matrix to an identity matrix. The circuit may only contain *gates*, if it contains *resets* or *measure* operations an exception will be raised.\n", - "\n", - "### Simulating a quantum circuit unitary\n", - "\n", - "For this example we will return the unitary matrix corresponding to the previous examples circuit which prepares a bell state." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Circuit unitary:\n", - " [[ 0.70710678+0.j 0.70710678+0.j 0. +0.j 0. +0.j]\n", - " [ 0. +0.j 0. +0.j 0.70710678+0.j -0.70710678+0.j]\n", - " [ 0. +0.j 0. +0.j 0.70710678+0.j 0.70710678+0.j]\n", - " [ 0.70710678+0.j -0.70710678+0.j 0. +0.j 0. +0.j]]\n" - ] - } - ], - "source": [ - "# Construct an empty quantum circuit\n", - "qr = QuantumRegister(2)\n", - "circ = QuantumCircuit(qr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "\n", - "# Select the UnitarySimulator from the Aer provider\n", - "simulator = Aer.get_backend('unitary_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator).result()\n", - "unitary = result.get_unitary(circ)\n", - "print(\"Circuit unitary:\\n\", unitary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting a custom initial unitary\n", - "\n", - "we may also set an initial state for the `UnitarySimulator`, however this state is an initial *unitary matrix* $U_i$, not a statevector. In this case the return unitary will be $U.U_i$ given by applying the circuit unitary to the initial unitary matrix.\n", - "\n", - "**Note:**\n", - "* The initial unitary must be a valid unitary matrix $U^\\dagger.U =\\mathbb{1}$. If not an exception will be raised. \n", - "* If a Qobj contains multiple experiments, the initial unitary must be the correct size fo *all* experiments in the Qobj, otherwise an exception will be raised.\n", - "\n", - "Let us consider preparing the output unitary of the previous circuit as the initial state for the simulator:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial Unitary:\n", - " [[ 0.70710678+0.j 0.70710678+0.j 0. +0.j 0. +0.j]\n", - " [ 0. +0.j 0. +0.j 0.70710678+0.j -0.70710678+0.j]\n", - " [ 0. +0.j 0. +0.j 0.70710678+0.j 0.70710678+0.j]\n", - " [ 0.70710678+0.j -0.70710678+0.j 0. +0.j 0. +0.j]]\n" - ] - } - ], - "source": [ - "# Construct an empty quantum circuit\n", - "qr = QuantumRegister(2)\n", - "circ = QuantumCircuit(qr)\n", - "circ.iden(qr)\n", - "\n", - "# Set the initial unitary\n", - "opts = {\"initial_unitary\": np.array([[ 1, 1, 0, 0],\n", - " [ 0, 0, 1, -1],\n", - " [ 0, 0, 1, 1],\n", - " [ 1, -1, 0, 0]] / np.sqrt(2))}\n", - "\n", - "# Select the UnitarySimulator from the Aer provider\n", - "simulator = Aer.get_backend('unitary_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator, backend_options=opts).result()\n", - "unitary = result.get_unitary(circ)\n", - "unitary = result.get_unitary(circ)\n", - "print(\"Initial Unitary:\\n\", unitary)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (qiskit-dev)", - "language": "python", - "name": "qiskit-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/custom_unitary_gates.ipynb b/examples/custom_unitary_gates.ipynb deleted file mode 100644 index 8525161d10..0000000000 --- a/examples/custom_unitary_gates.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Note: Trusted Notebook\" width=\"500 px\" align=\"left\">" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Qiskit Aer: Applying noise to custom gates\n", - "\n", - "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n", - "\n", - "**NOTE: THIS ONLY WORKS ON QISKIT-TERRA 0.7, QISKIT-AER 0.1**\n", - "\n", - "***\n", - "### Contributors\n", - "Christopher J. Wood$^{1}$\n", - "\n", - "1. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook shows how to use some experimental noise features to define a noise model for any unitary gate, not just the ones supported by the backend.\n", - "\n", - "**Disclaimer:** This is considered *experimental* as it involves modifying a compiled Qobj since the featurs are not yet supported through the Qiskit Terra `QuantumCircuit` API. As such they may change or break in future releases. Qiskit Aer includes some tools to help do this in the `qiskit_aer.utils.qobj_utils` module" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Import Qiskit\n", - "from qiskit_aer import Aer\n", - "from qiskit_aer.noise import NoiseModel\n", - "from qiskit_aer.noise.errors import amplitude_damping_error\n", - "from qiskit import compile, QuantumCircuit, QuantumRegister, ClassicalRegister\n", - "from qiskit.tools.visualization import plot_histogram\n", - "\n", - "# Import experimental Qobj utilities\n", - "from qiskit.qobj import QobjInstruction\n", - "from qiskit_aer.utils.qobj_utils import get_instr_pos\n", - "from qiskit_aer.utils.qobj_utils import insert_instr\n", - "from qiskit_aer.utils.qobj_utils import unitary_instr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating a custom unitary gate\n", - "\n", - "The Qiskit Aer simulators support defining a custom unitary matrix QobjInstruction as\n", - "\n", - "```\n", - "QobjInstruction(name='unitary', qubits=qubits, params=matrix, label=label)\n", - "```\n", - "\n", - "Where `matrix` is the unitary matrix we wish to apply, `qubits` is the list of qubits the matrix acts on, and `label` is an optional label string we can later use to identify this matrix in a noise model.\n", - "\n", - "For example we can make a unitary `QobjInstruction` for a Controlled-NOT gate with qubit-0 as control, and qubit-1 as target as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "cx_mat = np.array([[1, 0, 0, 0],\n", - " [0, 0, 0, 1],\n", - " [0, 0, 1, 0],\n", - " [0, 1, 0, 0]])\n", - "custom_cx = QobjInstruction(name='unitary', params=cx_mat,\n", - " qubits=[0, 1], label='custom_cx')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or equivalently using the convenience function from `qobj_utils.unitary_instr`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "QobjInstruction(label='custom_cx', name='unitary', params=array([[1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],\n", - " [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j],\n", - " [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]]), qubits=[0, 1])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unitary_instr(cx_mat, [0, 1], label='custom_cx')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** The matrix is specified with respect to the tensor product $U_{b}\\otimes U_{a}$ for `qubits=[a, b]`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example: using the iSWAP gate to implement a CX gate\n", - "\n", - "Lets consider an explicit example of using the unitary instruction for a custom gate. We will use the iSWAP gate which is an entangling two-qubit gate given by\n", - "\n", - "$$\\mbox{iSWAP} = \\left(\\begin{array} \n", - "& 1 & 0 & 0 & 0 \\\\ \n", - "0 & 0 & i & 0 \\\\ \n", - "0 & i & 0 & 0 \\\\ \n", - "0 & 0 & 0 & 1\n", - "\\end{array}\\right)$$\n", - "\n", - "A CX gate can be implemented using two iSWAP gates and single qubit gates with the following circuit:\n", - "\n", - "FIGURE TODO\n", - "\n", - "Since we cannot insert this instruction directly using the `QuantumCircuit` API we will create the circuit with a `barrier` instruction inserted everywhere the iSWAP gate should be:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NOTE: iSWAP gates should be inserted at barrier positions\n", - " ┌─────┐ ░ ┌─────┐┌───┐┌─────┐ ░ \n", - "qr_0: |0>────────────┤ Sdg ├─░─┤ Sdg ├┤ H ├┤ Sdg ├─░──────\n", - " ┌─────┐┌───┐└─────┘ ░ └─────┘└───┘└─────┘ ░ ┌───┐\n", - "qr_1: |0>┤ Sdg ├┤ H ├────────░─────────────────────░─┤ S ├\n", - " └─────┘└───┘ ░ ░ └───┘\n", - " cr_0: 0 ═════════════════════════════════════════════════\n", - " \n", - " cr_1: 0 ═════════════════════════════════════════════════\n", - " \n" - ] - } - ], - "source": [ - "qr = QuantumRegister(2, 'qr')\n", - "cr = ClassicalRegister(2, 'cr')\n", - "cx_circ = QuantumCircuit(qr, cr, name='cx')\n", - "\n", - "# CNOT Circuit where iSWAP gates should be inserted at barrier locations\n", - "cx_circ.sdg(qr[1])\n", - "cx_circ.h(qr[1])\n", - "cx_circ.sdg(qr[0])\n", - "cx_circ.barrier(qr) # Place iSWAP here\n", - "cx_circ.sdg(qr[0])\n", - "cx_circ.h(qr[0])\n", - "cx_circ.sdg(qr[0])\n", - "cx_circ.barrier(qr) # Place iSWAP here\n", - "cx_circ.s(qr[1])\n", - "\n", - "print(\"NOTE: iSWAP gates should be inserted at barrier positions\")\n", - "print(cx_circ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to compile the circuit and insert iSWAP instructions at the barrier positions in the `QobjExperiment`. \n", - "The `qobj_utils` includes functions to help do this (*see their docstrings for more details*):\n", - "\n", - "* `get_instr_pos` will return the positions of all instructions with a given name in a given `QobjExperiment`.\n", - "* `insert_instr` will insert a `QobjInstruction` into a given `QobjExperiment` at a specific location.\n", - "\n", - "Using these we can write a helper function to insert iSWAP gates after barrier operations. We will use the string label `\"iswap\"` for these gates so we cn assign a noise model to it later." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def add_iswap_after_barrier(qobj):\n", - " \"\"\"Inserts iSWAP unitary instructions after barriers.\"\"\"\n", - " iswap_mat = np.array([[1, 0, 0, 0],\n", - " [0, 0, 1j, 0],\n", - " [0, 1j, 0, 0],\n", - " [0, 0, 0, 1]])\n", - " instr = unitary_instr(iswap_mat, [0, 1], 'iswap')\n", - " for exp_pos, _ in enumerate(qobj.experiments):\n", - " for pos in reversed(get_instr_pos(qobj, exp_pos, 'barrier')):\n", - " insert_instr(qobj, exp_pos, instr, pos)\n", - " return qobj" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verifying circuit on the Aer unitary simulator\n", - "\n", - "We will execute the circuit on the Aer unitary simulator to verify our qobj implements the CX gate" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Circuit unitary:\n", - "[[1.+0.j 0.+0.j 0.+0.j 0.+0.j]\n", - " [0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n", - " [0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n", - " [0.+0.j 1.+0.j 0.+0.j 0.+0.j]]\n" - ] - } - ], - "source": [ - "# Select UnitarySimulator backend\n", - "simulator = Aer.get_backend('unitary_simulator')\n", - "\n", - "# Compile the circuit qobj and insert iSWAP instructions\n", - "qobj = compile(cx_circ, simulator,)\n", - "qobj = add_iswap_after_barrier(qobj)\n", - "\n", - "# Execute on the simulator\n", - "result = simulator.run(qobj).result()\n", - "unitary = result.get_unitary(cx_circ)\n", - "print(\"Circuit unitary:\")\n", - "print(unitary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So we have confirmed that our circuit implemented the correct unitary!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using a custom unitary in a noise model\n", - "\n", - "Now that we have executed a qobj that includes a custom unitary instruction, we can now assign a quantum error to this gate in a `NoiseModel`. To do this we simply use our unitary instruction label `iswap` for the gate name when adding a quantum error to the noise model. For our example we will create an 2-qubit error consisting of two single-qubit amplitude damping channels with different damping parameters. For now we will assume all the other circuit instructions are ideal" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Error parameters\n", - "param_q0 = 0.05 # damping parameter for qubit-0\n", - "param_q1 = 0.1 # damping parameter for qubit-1\n", - "p_state1 = 0.03 # |1> state equilibrium population for damping\n", - "\n", - "# Construct the error\n", - "qerror_q0 = amplitude_damping_error(param_q0, excited_state_population=p_state1)\n", - "qerror_q1 = amplitude_damping_error(param_q1, excited_state_population=p_state1)\n", - "iswap_error = qerror_q1.kron(qerror_q0)\n", - "\n", - "# Build the noise model by adding the error to the \"iswap\" gate\n", - "noise_model = NoiseModel()\n", - "noise_model.add_all_qubit_quantum_error(iswap_error, 'iswap')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first take our previous CX circuit and add an initial Hadamard gate and final measurement to create a Bell-state preparation circuit that we may simulator on the `QasmSimulator` both for the ideal and noisy case" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bell preparation circuit: iSWAPS should be inserted at barrier positions\n", - " ┌───┐┌─────┐ ░ ┌─────┐┌───┐┌─────┐ ░ ┌─┐ \n", - "qr_0: |0>────────────┤ H ├┤ Sdg ├─░─┤ Sdg ├┤ H ├┤ Sdg ├─░─┤M├────────\n", - " ┌─────┐┌───┐└───┘└─────┘ ░ └─────┘└───┘└─────┘ ░ └╥┘┌───┐┌─┐\n", - "qr_1: |0>┤ Sdg ├┤ H ├─────────────░─────────────────────░──╫─┤ S ├┤M├\n", - " └─────┘└───┘ ░ ░ ║ └───┘└╥┘\n", - " cr_0: 0 ══════════════════════════════════════════════════╩═══════╬═\n", - " ║ \n", - " cr_1: 0 ══════════════════════════════════════════════════════════╩═\n", - " \n" - ] - } - ], - "source": [ - "# Bell state circuit where iSWAPS should be inserted at barrier locations\n", - "bell_circ = QuantumCircuit(qr, cr, name='bell')\n", - "bell_circ.h(qr[0])\n", - "bell_circ = bell_circ + cx_circ \n", - "bell_circ.measure(qr, cr)\n", - "print(\"Bell preparation circuit: iSWAPS should be inserted at barrier positions\")\n", - "print(bell_circ)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again we must add iSWAP gates back into the compiled qobj for execution on the Aer Qasm simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Select UnitarySimulator backend\n", - "simulator = Aer.get_backend('qasm_simulator')\n", - "\n", - "# Compile the circuit qobj and insert iSWAP instructions\n", - "qobj = compile(bell_circ, simulator,)\n", - "qobj = add_iswap_after_barrier(qobj)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ideal output\n", - "\n", - "Lets first see the ideal output. Since this generates a Bell-state we expect two peaks for 00 and 11" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Execute on the simulator without noise\n", - "result = simulator.run(qobj).result()\n", - "ideal_counts = result.get_counts(bell_circ)\n", - "plot_histogram(ideal_counts, title='Ideal output for iSWAP bell-state preparation')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Noisy circuit execution\n", - "\n", - "Finally lets now simulate it with our custom noise model. Since there is a small amplitude damping error on the two qubit gates we expect small additional peaks for the 01 and 10 outcome probabilities" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Execute on the simulator without noise\n", - "result = simulator.run(qobj, noise_model=noise_model).result()\n", - "noise_counts = result.get_counts(bell_circ)\n", - "plot_histogram(noise_counts, title='Noisy output for iSWAP bell-state preparation')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (qiskit-dev)", - "language": "python", - "name": "qiskit-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/device_noise_simulation.ipynb b/examples/device_noise_simulation.ipynb deleted file mode 100644 index ee83f19664..0000000000 --- a/examples/device_noise_simulation.ipynb +++ /dev/null @@ -1,337 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Note: Trusted Notebook\" width=\"500 px\" align=\"left\">" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Qiskit Aer: Device noise model simulations\n", - "\n", - "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n", - "\n", - "**NOTE: THIS ONLY WORKS ON QISKIT-TERRA 0.7, QISKIT-AER 0.1**\n", - "\n", - "***\n", - "### Contributors\n", - "Christopher J. Wood$^{1}$\n", - "\n", - "1. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook shows how to use the Qiskit Aer `noise.device` module to automatically generate a noise model for an IBMQ hardware device, and use this model to do noisy simulation of `QuantumCircuits` to study the effects of errors which occur on real devices.\n", - "\n", - "Note that these automatic models are only a highly simplied approximation of the real errors which occur on actual devices. The study of quantum errors on real devices is an active area of research and we discuss the Qiskit Aer tools for configuring more detailed noise models in another notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Import Terra\n", - "import qiskit\n", - "from qiskit import IBMQ, execute\n", - "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n", - "from qiskit.tools.visualization import plot_histogram\n", - "from qiskit.tools.monitor import job_monitor\n", - "\n", - "# Import Aer\n", - "from qiskit_aer import Aer\n", - "from qiskit_aer.noise.device import basic_device_noise_model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Device noise module\n", - "\n", - "The *Qiskit Aer* device noise module contains two functions to automatically generate a simplified noise model for a real device. This model is generated using the calibration information reported in the `BackendProperties` of a device.\n", - "\n", - "### IBMQ Provider\n", - "\n", - "We will use a real hardware device in the `IBMQ` provider as an example. First we must load our account credentials, and then select a backend from the provider." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load IBMQ account\n", - "IBMQ.load_accounts()\n", - "IBMQ.backends()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use the `ibmq_16_melbourne` device for this tutorial. We may get the properties of the backend using the `properties` method, the information in the returned `BackendProperties` object will be used to automatically generate a noise model for the device that can be used by the Qiskit Aer `QasmSimulator`. We will also want to get the `coupling_map` for the device from its `configuration` to use when compiling circuits for simulation to most closely mimic the gates that will be executed on a real device" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "device = IBMQ.get_backend('ibmq_16_melbourne')\n", - "properties = device.properties()\n", - "coupling_map = device.configuration().coupling_map" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test circuit for device and simulation comparison\n", - "\n", - "Now we construct a test circuit to compare the output of the real device with the noisy output simulated on the Qiskit Aer `QasmSimulator`. We will prepare a 3-qubit GHZ state $\\frac{1}{2}(|0,0,0\\rangle + |1,1,1\\rangle)$ on qubits 0, 1 and 2. Before running with noise or on the device we show the ideal expected output with no noise." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct quantum circuit\n", - "qr = QuantumRegister(3, 'qr')\n", - "cr = ClassicalRegister(3, 'cr')\n", - "circ = QuantumCircuit(qr, cr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "circ.cx(qr[1], qr[2])\n", - "circ.measure(qr, cr)\n", - "\n", - "# Select the QasmSimulator from the Aer provider\n", - "simulator = Aer.get_backend('qasm_simulator')\n", - "\n", - "# Execute and get counts\n", - "result = execute(circ, simulator).result()\n", - "counts = result.get_counts(circ)\n", - "plot_histogram(counts, title='Ideal counts for 3-qubit GHZ state')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating a device noise model\n", - "\n", - "Noise models in Qiskit Aer are presented using the `NoiseModel` object from the `aer.noise` module. The function we will use is the `basic_device_noise_model` function from `aer.noise.device` module which will return a basic approximate `NoiseModel` object configured from a device `BackendProperties`.\n", - "\n", - "### Basic device noise model\n", - "\n", - "The `basic_device_noise_model` constructs an approximate noise model consisting of:\n", - "\n", - "* **Single-qubit gate errors** consisting of a single qubit depolarizing error followed by a single qubit thermal relaxation error.\n", - "* **Two-qubit gate errors** consisting of a two-qubit depolarizing error followed by single-qubit thermal relaxation errors on both qubits in the gate.\n", - "* **Single-qubit readout errors** on the classical bit value obtained from measurements on invidiual qubits\n", - "\n", - "For the gate errors the error parameter of the thermal relaxation errors is dervied using the `thermal_relaxation_error` function from `aer.noise.errors` moduel, along with the individual qubit $T_1$ and $T_2$ paramters, and the `gate_time` parameter from the device backend properties. The probability of the depolarizing error is then set so that the combined average gate infidelity from the depolarizing error followed by the thermal relaxaxtion is equal to the `gate_error` value from the backend properties.\n", - "\n", - "For the readout errors the probability that the recorded classical bit value will be flipped from the true outcome after a measurement is given by the qubit `readout_errors`.\n", - "\n", - "Let us construct the device noise model.\n", - "\n", - "**Note:** *Since the devices don't currently provide the gate times for gates we will manually provide them for the gates we are interestied in using the optional `gate_times` argument for `basic_device_noise_model`.*" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NoiseModel:\n", - " Instructions with noise: ['u3', 'measure', 'u2', 'cx']\n", - " Specific qubit errors: [('u2', [0]), ('u2', [1]), ('u2', [2]), ('u2', [3]), ('u2', [4]), ('u2', [5]), ('u2', [6]), ('u2', [7]), ('u2', [8]), ('u2', [9]), ('u2', [10]), ('u2', [11]), ('u2', [12]), ('u2', [13]), ('u3', [0]), ('u3', [1]), ('u3', [2]), ('u3', [3]), ('u3', [4]), ('u3', [5]), ('u3', [6]), ('u3', [7]), ('u3', [8]), ('u3', [9]), ('u3', [10]), ('u3', [11]), ('u3', [12]), ('u3', [13]), ('cx', [1, 0]), ('cx', [1, 2]), ('cx', [2, 3]), ('cx', [4, 3]), ('cx', [4, 10]), ('cx', [5, 4]), ('cx', [5, 6]), ('cx', [5, 9]), ('cx', [6, 8]), ('cx', [7, 8]), ('cx', [9, 8]), ('cx', [9, 10]), ('cx', [11, 3]), ('cx', [11, 10]), ('cx', [11, 12]), ('cx', [12, 2]), ('cx', [13, 1]), ('cx', [13, 12]), ('measure', [0]), ('measure', [1]), ('measure', [2]), ('measure', [3]), ('measure', [4]), ('measure', [5]), ('measure', [6]), ('measure', [7]), ('measure', [8]), ('measure', [9]), ('measure', [10]), ('measure', [11]), ('measure', [12]), ('measure', [13])]\n" - ] - } - ], - "source": [ - "# List of gate times for ibmq_14_melbourne device\n", - "# Note that the None parameter for u1, u2, u3 is because gate\n", - "# times are the same for all qubits\n", - "gate_times = [\n", - " ('u1', None, 0), ('u2', None, 100), ('u3', None, 200),\n", - " ('cx', [1, 0], 678), ('cx', [1, 2], 547), ('cx', [2, 3], 721),\n", - " ('cx', [4, 3], 733), ('cx', [4, 10], 721), ('cx', [5, 4], 800),\n", - " ('cx', [5, 6], 800), ('cx', [5, 9], 895), ('cx', [6, 8], 895),\n", - " ('cx', [7, 8], 640), ('cx', [9, 8], 895), ('cx', [9, 10], 800),\n", - " ('cx', [11, 10], 721), ('cx', [11, 3], 634), ('cx', [12, 2], 773),\n", - " ('cx', [13, 1], 2286), ('cx', [13, 12], 1504), ('cx', [], 800)\n", - "]\n", - "\n", - "# Construct the noise model from backend properties\n", - "# and custom gate times\n", - "noise_model = basic_device_noise_model(properties, gate_times=gate_times)\n", - "print(noise_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating a quantum circuit with noise\n", - "\n", - "To use this noise model we must make use of several keyword args in the `execute` function. These are:\n", - "\n", - "* `noise_model`: This passes the noise model to the `QasmSimulator.run` method for noisy simulation.\n", - "* `basis_gates`: A noise model is defined with respect to specific gates, we must pass these basis gates to the Qiskit compiler so that it compiles the circuit to the correct gates for the noise model. The basis gates of a noise model may be obtained from the `NoiseModel.basis_gates` property.\n", - "* `coupling_map`: We also must make sure we provide the `coupling_map` for the real device so that the compiler will produce a Qobj for the simulator that will match the compiled experiment that can be executed on the real device." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SaIYqKyvZZ599at6XlJRQWVm5XZl33nmHpUuXctRRR+3w+enTp3PooYdy2GGHcfPNN9ck1GoTJkzglFNOaZzgRUQka5RAM+TuOwxLPJP8/PPP+cUvfsENN9yQ8vODBw9m2rRpTJo0ibFjx7Jp06aacVu2bOH//u//OPHEE7MfuIiIZJUSaIZKSkpYunRpzftly5ax995717z/9NNPqaio4Pjjj2fgwIFMnz6ds88+u6YhUbVevXrRtm1bKioqaoZNmjSJAQMGsNdeezX+jIiISIMogWbo4IMPZsGCBSxatIgtW7YwYcIEjj766JrxHTp0YP78+ZSXl1NeXs7gwYP561//SllZGYsWLWLbtm0ALF68mPnz51NaWlrz2fHjx6v6VkSkQKgVboaaN2/OjTfeyKmnnkpVVRVnn302ffr0YcyYMZSVlXHMMcek/ezrr7/O2LFjadGiBbvttht//OMf6dy5MwAbNmzgX//6F7feemtTzYqIiDSApbqmt6soKyvzyZMn5zoMERHJI506dZrh7oPrKqcqXBERaXJxO6R56aWXOOKIIxg2bBhHHHFEzT30AG+//TbDhg1j0KBB/PznP0/Z6DObmjyBmtkPzGyhmW0ysxlmNryenzvMzLaZ2btJw883M0/xap1uWiIikjvVHdI89thjTJs2jfHjx/Pee+/tUC5VhzSdO3fmkUceYerUqdx2221ccsklNeOuuOIKbr31VqZPn87777/PpEmTGnU+mjSBmtkZwJ+AMUAZ8Bow0cxK6/hcR+BB4J9pimwAihNf7r4pTVkREcmhhnRIM2DAAIqLi4HQu9umTZvYvHkzH330EevXr+eQQw7BzDjzzDN54YUXGnU+mvoM9HLgfncf5+4V7v5joBK4pI7P3Qs8AExLM97d/aPEVxZjFhGRLGpohzTVnnnmGQYMGECrVq2orKykpKSk1mlmW5MlUDNrCQwCXkwa9SLwlVo+9wNgbyB1zwRBGzNbZGZLzOw5MytrcMAiItIoGtohDUBFRQXXXXcdt9xyS72m2Ria8jaWPYFmwPKk4cuBI1N9wMwOBK4Fhrp7VZofYy5wIVAOtAcuA6aa2UB3n5dimhcBFwEUFxfz1ltvAeFopW3btsyfPx+A3XffnR49etRcuG7WrBkDBw5k7ty5fPbZZ0CoPli9ejXLl4dZ2nfffWnZsiULFiwAoGPHjpSWllJeXg5AixYtOPDAA6moqGDjxo0A9O3blxUrVrBixQoA9ttvP8yMDz74AAj1/cXFxbz7brj026pVK/r168fs2bPZvHkzAP3796eyspJVq1YB0K1bN9ydRYsWAVBUVERRURFz5swBoE2bNvTp04dZs2axdetWAAYOHMiHH37ImjVrAOjRowdbtmxhyZIlAHTp0oVOnTrVdPzQrl07evXqRXl5OVVVVQCUlZWxYMEC1q5dC0DPnj3ZsGEDy5Yto/r37tChA3PnzgWgffv2HHDAAcycORN3x8woKytj3rx5rF+/HggdTqxbt67mSFLLSctJy6nwl9Oee+7J+++/X7P/nT9/Ph06dKh537JlSyoqKmrOPtesWcPZZ5/Nb37zG7p3786KFSv4xS9+wbXXXsuaNWtYs2YNbdu2ZfHixTXTmDVrFkVFRTXvM1lO9dVkt7GYWQmwFBjh7q8kDL8WOMvdeyeVbwW8Bfze3R+Kho0GTnX3/rV8TzPgbeAld7+0tph0G4uISNPbtm0bQ4YM4amnnqK4uJiRI0dy991306dPn5Tljz/+eK6//nrKyspYu3Ytxx13HD/72c844YQTtis3cuRIfv/73zN48GBOP/10LrroIkaNGpVxfPl4G8tKoIpQHZtoL3Y8K4XQGKgvcF/U+nYb8GugX/T+66m+xN2rgOnAAVmLXEREsiaxQ5qhQ4dy0kkn1XRIk6oxUaJx48axcOFCbrrppppnKFfXONx0001cdtllDBo0iO7du3PkkSkrN7OmSTtSMLM3gHJ3vyhh2H+A8e5+dVLZFkCvpEn8ABgFfBP4wN0/TfEdRkig5e5+YW3x6AxURESS1fcMtKm78rsFeMjM3gSmAhcDJcCdAGb2IIC7n+fuW4Hkez4/Bja7+7sJw64FXgfmAR2AS4EB1N2yV0REJLYmTaDu/jcz6wz8klBF+y5wrLsviorUej9oGnsAdxOqhtcCMwnXWd/MQsgiIiIpqS9cVeGKiEiCfK3C3Sld+UDHBk/jxm+vyUIkIiLSVNSZvIiISAxKoCIiIjEogYqIiMSgBCoiIhKDEqiIiEgMSqAiIiIxKIGKiIjEoAQqIiISgzpSEBGRnCvEDml0BioiIhKDEqiIiEgMSqAiIiIxKIGKiIjEoAQqIiISgxKoiIhIDEqgIiIiMSiBioiIxKAEKiIiEoMSqIiISAxKoCIiIjEogYqIiMSgBCoiIhKDEqiIiEgMSqAiIiIxKIGKiIjEoAQqIiISgxKoiIhIDEqgIiIiMSiBioiIxKAEKiIiEoMSqIiISAxKoCIiIjEogYqIiMSgBCoiIhKDEqiIiEgMSqAiIiIxKIGKiIjEoAQqIiISgxKoiIhIDEqgIiIiMSiBioiIxKAEKiIiEoMSqIiISAxKoCIiIjEogYqIiMSgBCoiIhKDEqiIiEgMSqAiIiIxKIGKiIjEoAQqIiISQ0YJ1MxON7OvJ7z/tZktMbO/m1lx9sMTERHJT5megY6u/sfMDgauAf4HaAHcnL2wRERE8lvzDMvvB8yN/v8m8JS732hmLwJ/z2pkIiIieSzTM9BNQPvo/5HApOj/tQnDRUREdnqZnoG+AtxsZq8Cg4FTo+FfBhZnMzAREZF8lukZ6I+ALYTEebG7L4uGH4OqcEVEZBeS0Rmouy8Bjk8x/CdZi0hERKQAZHwfqJm1NrNTzewqM9sjGra/mXXKfngiIiL5KaMzUDPrSWg49CVgD+Bx4BPgkuj9d7MdoIiISD7K9Ax0LPAi0AXYmDD8GeCIbAUlIiKS7zJthfsVYKi7V5lZ4vAPgZKsRSUiIpLn4vSF2yLFsFLCvaAiIiK7hEwT6IvA5Qnv3cw6ANcBz2ctKhERkTyXaRXu5cBLZjYXaA38DegJLAdOz3JsIiIieSvT+0CXmdlBwFnAwYQz2LuBv7r7xlo/LCIishPJ9AyUKFH+JXqJiIjskuq8BmpmJ5tZi4T/077q84Vm9gMzW2hmm8xshpkNr6XsV83sNTNbZWYbzew9M7siRblTzGyOmW2O/n6zPrGIiIjEVZ8z0CeAvYGPo//TcaBZbRMyszOAPwE/AF6N/k40s77u/mGKj3xKeN7oLGADMAy4y8w2uPvt0TQPJVyLvRaYAJwMPG5mw9z9jXrMn4iISMbqPAN1993c/eOE/9O9ak2ekcuB+919nLtXuPuPgUpCT0apvnuGuz/q7rPdfaG7P0zotD7xrPUnwEvu/ttomr8F/hUNFxERaRQZ3cZiZiPMbIezVjNrZmYj6vhsS2AQ4VaYRC8SOmioz/eXRWVfThh8aIpp/r2+0xQREYkj00ZELwHFhOrcRHtE42o7C90zGr88afhy4MjavtTMlgBFhHivc/c7E0bvnWaae6eZ1kXARQDFxcW89dZbAJSUlNC2bVvmz58PwO67706PHj2YOXMmAM2aNWPgwIHMnTuXzz77DIA+ffqwevVqoGNt4ddLdRz77bcfZsYHH3wAQOfOnSkuLubdd98FoFWrVvTr14/Zs2ezefNmAPr3709lZSWrVq0CoFu3brg7ixYtAqCoqIiioiLmzJkDQJs2bejTpw+zZs1i69atAAwcOJAPP/yQNWvWANCjRw+2bNnCkiVLAOjSpQudOnWioqICgHbt2tGrVy/Ky8upqqoCoKysjAULFrB2behTo2fPnmzYsIFly8JT74qLi+nQoQNz584FoH379hxwwAHMnDkTd8fMKCsrY968eaxfvx6AXr16sW7dOiorKxu8nJYvD6vJvvvuS8uWLVmwYAEAHTt2pLS0lPLycgBatGjBgQceSEVFBRs3hsblffv2ZcWKFaxYsULLSctJy6kRllM29qOrVq3KynKqL3P3+hc2+xzo4u4rkoZ/GZju7h1q+WwJsBQY4e6vJAy/FjjL3XvX8tnuhA7shwJ/AC5z94eicVuA71S/j4Z9G7jL3VvXNj9lZWU+efLk2orUy5UPNHzB3/jtNQ2ehohIocqn/WinTp1muPvgusrV6wzUzJ6J/nXgYTPbnDC6GdAfeK2OyawEqtjxzHAvdjyD3I67L4z+nWVmXYDRQHXC/CjONEVERBqivtdAV0UvA9YkvF8FLAHuBM6pbQLuvgWYAYxKGjWKupNvot2AVgnvp2VhmiIiIhmp1xmou18AYGYfADe5+2cxv+8W4CEzexOYClxMeIrLndH0H4y+77zo/Y+BhcDc6PMjgCuA2xOm+SdgipldDTwJfJPwaLXDYsYoIiJSp0y78ruuIV/m7n8zs87ALwmNkd4FjnX3RVGR0qSPNCNc8+wGbAPeB35OlHCjab5mZmcCNxA6tX8fOEP3gIqISGOqM4Ga2TvAV919jZnNIlwHTcndB9Q1vagDhNvTjDs86f1YwkO865rmE9TeyYOIiEhW1ecMdDxQ3WhISUpERIR6JNDEatuGVuGKiIjsLDJ9oLaIiIhQv2ugtV73TFSfa6AiIiI7g/o+jUVEREQSZHQNVERERAJdAxUREYmhye8DFRER2RnoPlAREZEYdB+oiIhIDJk+UBsAM9sf6BO9rXD397MXkoiISP7LKIFGHcHfC5wAfP7FYHsOuNDdV2U5PhERkbyUaSvce4CewHCgdfQaAXQHxmU3NBERkfyVaRXuUcBId5+WMGyqmX0fmJS9sERERPJbpmegK4BUD9PeAKj6VkREdhmZJtDrgbFmtk/1gOj/m6NxIiIiu4Q4ncl3Bz4ws6XR+32ATcBehGukIiIiOz11Ji8iIhKDOpMXERGJQZ3Ji4iIxJBRAjWzlmZ2nZn9x8w2mVlV4quxghQREck3mZ6B/gb4NqHV7efAz4DbCLew/CC7oYmIiOSvTBPo6cDF7n4XUAU87e6XAtcCo7IdnIiISL7KNIF2AeZE/38K7BH9/3/A17MVlIiISL7LNIF+CJRE/88ndO0HcCiwMVtBiYiI5LtME+iTwMjo/z8B15nZQuB+1ImCiIjsQjLqTN7dr074/wkzWwJ8BfiPuz+X7eBERETyVawHaldz99eB17MUi4iISMHIuCMFMzvYzB40s+nR6yEzO7gxghMREclXmXakcDbwb6AYeCF6dQHeNLNzsh+eiIhIfsq0Cve3wK/cfUziQDO7GrgBeDhbgYmIiOSzTKtwi4DHUgx/nPA4MxERkV1Cpgn0JeDwFMMPB15uaDAiIiKFoj4P1D454e1E4HdmNpgvWt8OBU4GRmc9OhERkTwV94HaF0WvRH8Gbm9wRCIiIgWgPg/U1jNDRUREkig5ioiIxBCnI4VvmNkUM1tpZivM7GUzO7YxghMREclXmXak8F1Ch/LvA1cBPwcWAk+a2YXZD09ERCQ/ZdqRwlXA5e7+/xKG3WtmMwjJ9C9Zi0xERCSPZVqFW0p4eHayicB+DQ9HRESkMMR5oPaoFMO/DixqeDgiIiKFIdMq3JuAP0dPX3kNcOAw4Fzgx1mOTUREJG9l+kDtu8zsY+C/Cb0PAVQAp7v709kOTkREJF/VO4GaWXNCVe0Ud3+y8UISERHJf/W+Buru24AJQPvGC0dERKQwZNqIqBzo2RiBiIiIFJJME+ho4GYzO8nMuppZp8RXI8QnIiKSlzJthft89HcCoQVuNYveN8tGUCIiIvku0wR6RKNEISIiUmDqlUDNrC3wR+AkoAUwCbjU3Vc2YmwiIiJ5q77XQK8DzidU4f4voTeiOxopJhERkbxX3yrck4HvuPujAGb2V2CqmTVz96pGi05ERCRP1fcMtCvwSvUbd38T2AaUNEZQIiIi+a6+CbQZsCVp2DYyb4QkIiKyU6hvAjTgYTPbnDCsNTDOzDZUD3D3E7IZnIiISL6qbwJ9IMWwh7MZiIiISCGpVwJ19wsaOxAREZFCkmlXfiIiIoISqIiISCxKoCIiIjEogYqIiMSgBCoiIhKDEqiIiEgMSqAiIiIxKIGKiIgZPpp8AAAa2ElEQVTEoAQqIiISQ5MnUDP7gZktNLNNZjbDzIbXUrbYzB4xs/fMrMrM7k9R5nwz8xSv1o06IyIisktr0gRqZmcAfwLGAGXAa8BEMytN85FWwErg98AbtUx6A1Cc+HL3TdmKW0REJFlTn4FeDtzv7uPcvcLdfwxUApekKuzuH7j7pe5+P7C6lum6u3+U+Mp+6CIiIl9osgRqZi2BQcCLSaNeBL7SwMm3MbNFZrbEzJ4zs7IGTk9ERKRWTflA7D0JD+ZenjR8OXBkA6Y7F7gQKAfaA5cBU81soLvPSy5sZhcBFwEUFxfz1ltvAVBSUkLbtm2ZP38+ALvvvjs9evRg5syZADRr1oyBAwcyd+5cPvvsMwD69OnD6tWrgY4NCD+ojmO//fbDzPjggw8A6Ny5M8XFxbz77rsAtGrVin79+jF79mw2bw6PZ+3fvz+VlZWsWrUKgG7duuHuLFq0CICioiKKioqYM2cOAG3atKFPnz7MmjWLrVu3AjBw4EA+/PBD1qxZA0CPHj3YsmULS5YsAaBLly506tSJiooKANq1a0evXr0oLy+nqqoKgLKyMhYsWMDatWsB6NmzJxs2bGDZsmVU/94dOnRg7ty5ALRv354DDjiAmTNn4u6YGWVlZcybN4/169cD0KtXL9atW0dlZWWDl9Py5WHV23fffWnZsiULFiwAoGPHjpSWllJeXg5AixYtOPDAA6moqGDjxo0A9O3blxUrVrBixQotJy0nLadGWE7Z2I+uWrUqK8upvszdGxx0vb7IrARYCoxw91cShl8LnOXuvev4/HPASnc/v45yzYC3gZfc/dLaypaVlfnkyZPrOQfpXflAwxf8jd9e0+BpiIgUqnzaj3bq1GmGuw+uq1xTXgNdCVQBeycN34sdz0pjc/cqYDpwQLamKSIikqzJEqi7bwFmAKOSRo0itMbNCjMzYAChcZKIiEijaMproAC3AA+Z2ZvAVOBioAS4E8DMHgRw9/OqP2BmB0X/dgA+j95vcfc50fhrgdeBeVGZSwkJNGXLXhERkWxo0gTq7n8zs87ALwn3a74LHOvui6Iiqe4HnZn0/nhgEdAter8HcDehanhtVH6Eu7+Z3ehFRES+0NRnoLj77cDtacYdnmKY1TG9nwI/zUpwIiIi9aS+cEVERGJQAhUREYlBCVRERCQGJVAREZEYlEBFRERiUAIVERGJQQlUREQkBiVQERGRGJRARUREYlACFRERiUEJVEREJAYlUBERkRiUQEVERGJQAhUREYlBCVRERCQGJVAREZEYlEBFRERiUAIVERGJQQlUREQkBiVQERGRGJRARUREYlACFRERiUEJVEREJAYlUBERkRiUQEVERGJQAhUREYlBCVRERCQGJVAREZEYlEBFRERiUAIVERGJQQlUREQkBiVQERGRGJRARUREYlACFRERiUEJVEREJAYlUBERkRiUQEVERGJQAhUREYlBCVRERCQGJVAREZEYlEBFRERiUAIVERGJQQlUJAcmTZrEIYccwqBBgxg7duwO4zdv3syFF17IoEGDOPLII/nwww8BmDFjBiNGjGDEiBEMHz6c5557brvPVVVV8dWvfpUzzzyzSeZDZFemBCrSxKqqqrjyyit57LHHmDZtGuPHj+e9997brszDDz/MHnvswYwZM7jkkksYPXo0AH369GHy5MlMmTKFxx9/nMsvv5xt27bVfO7OO+/ky1/+clPOjsguSwlUpInNmDGD7t27061bN1q2bMnJJ5/MxIkTtyvzwgsv1JxFnnjiiUyZMgV3p23btjRv3hwIZ6lmVvOZpUuX8o9//INzzz236WZGZBemBCrSxCorK9lnn31q3peUlFBZWZm2TPPmzenQoQOrV68GYPr06Rx66KEcdthh3HzzzTUJ9ZprrmH06NHstps2a5GmoC1NpIm5+w7DEs8k6yozePBgpk2bxqRJkxg7diybNm3i73//O0VFRRx00EGNE7SI7EAJVKSJlZSUsHTp0pr3y5YtY++9905bZtu2baxbt46OHTtuV6ZXr160bduWiooK3njjDSZOnMjAgQP57ne/yyuvvML3v//9xp8ZkV2YEuguKG4L0JdeeokjjjiCYcOGccQRRzBlypQdPvutb32Lr3zlK40+D4Xs4IMPZsGCBSxatIgtW7YwYcIEjj766O3KHHPMMTz66KMAPP300wwfPhwzY9GiRTWNhhYvXsz8+fMpLS3l17/+NbNnz6a8vJx77rmH4cOHc9dddzX5vBUKbQOSDUqgu5iGtADt3LkzjzzyCFOnTuW2227jkksu2e5zzz77LO3atWuqWSlYzZs358Ybb+TUU09l6NChnHTSSfTp04cxY8bUNCY655xzWL16NYMGDeKOO+7g2muvBeD1119n+PDhjBgxgnPPPZc//vGPdO7cucnnoTES0A033ED//v3p2rVro8a+s2wDhbwMdhaW6lrLrqKsrMwnT57c4Olc+UDHugvV4cZvr2nwNOrjzTff5A9/+APjx48H4NZbbwXgpz/9aU2ZU045hauuuopDDjmEbdu20bt3b+bNm7fddTp3p2fPnsyZM4dWrVrx6aefctppp3Hrrbdy4YUX8tprrzXJ/EjTq6qqYsiQIUyYMIGSkhJGjhzJuHHj6N27d02Ze++9l9mzZ3PLLbcwfvx4nn/+ef7yl7/wzjvvUFRURHFxMXPmzOG0005j9uzZAPz73/+ma9euDBkyhMWLFzda/DvDNlDoyyCVfNqPdurUaYa7D66rnM5AdzENbQFa7ZlnnmHAgAG0atUKgDFjxvDDH/6Qtm3bNvIcxD/yXr16NSeccAJdu3blyiuv3O4z48ePZ9iwYRx22GGceuqprFq1qtHno1A15DacAQMGUFxcDIR7Wjdt2sTmzZsBGDJkyA7XghvDzrANFPoy2Fkoge5iGtoCFKCiooLrrruOW265BYBZs2axcOFCjjvuuCxHu6OGVL+1atWKa665huuvv3678tu2bePqq6/mmWee4dVXX6Vfv36MGzeu0eelUDVWAmoqhb4NQOEvg51F81wHIE0rkxag++yzzw4tQJcuXcp5553H7bffTvfu3YFQ7VNeXs7AgQPZtm0bK1eu5Pjjj+fZZ5/NevyJR95AzZF3YtXVCy+8wFVXXQWEI++rrroKd6ddu3YMHTqUBQsWbDdNd8fd2bBhA506dWL9+vU189ZU8qn6qi7ZTEDV1ahNqdC3ASj8ZbCz0BnoLqYhLUDXrl3LmWeeya9+9SuGDh1aU/7CCy9kzpw5lJeXM3HiRPbff/9G23Fk68g7UYsWLbjpppsYNmwYffv2Ze7cuerNpxYNvQ0nVQJqSoW+DUDhL4OdhRLoLqYhLUDHjRvHwoULuemmm2o6NF+xYkWTxp+NI+9kW7du5b777uPll19mzpw59OvXr6ZhieyoMRJQUyr0bQAKfxnsLFSFuwsaNWoUo0aN2m7YNddcU/N/69atuf/++3f43BVXXMEVV1xR67RLS0sbtfVhQ6vfUpk1axZAzZH4SSedlLJxkgSJCaiqqoqzzz67JgGVlZVxzDHHcM4553DxxRczaNAgOnbsyD333ANsn4BuuukmIDTgKioq4tprr+WJJ55gw4YN9OvXj3PPPZef//znjTIPhbwNwM6xDHYGuo1lF7uNpdBt27aNIUOG8NRTT1FcXMzIkSO5++676dOnT02Ze+65hzlz5tQ033/uuee47777asY/8sgjvP3229x4441AqPL92te+xiuvvMKee+7Jb3/7WzZu3MgNN9zQZPOldUh2dfm0DdT3NhadgUperbh1aciRN8DAgQNZv349W7du5fnnn2f8+PH07t2bK6+8km984xu0aNGCrl27cttttzXJ/Ej+aOh2oAOYXY8SqBScuNVvAOXl5SmHX3DBBVxwwQVZi1FEdn5KoCLSYIVUi7Gz0jJoemqFKyIiEoPOQGWnoOtXItLUdAYqIiISgxKoiIhIDEqgIiIiMTR5AjWzH5jZQjPbZGYzzGx4HeW/GpXbZGYLzOzihk5TRESkoZo0gZrZGcCfgDFAGfAaMNHMStOU7w68EJUrA34H/NnMTok7TRERkWxo6jPQy4H73X2cu1e4+4+BSuCSNOUvBpa5+4+j8uOAB4DEzigznaaIiEiDNVkCNbOWwCDgxaRRLwJfSfOxQ1OU/zsw2MxaxJymiIhIgzXlGeieQDNgedLw5cDeOxaHaHiq8s2j6cWZpoiISIM12dNYzKwEWAqMcPdXEoZfC5zl7r1TfOY/wEPu/puEYV8F/gUUEw4AMp3mRcBF0dtewNyGz12d9gRWNsH3NKZCnwfFn3uFPg+KP/eaah72c/eiugo1ZU9EK4Eqdjwz3IsdzyCrfZSm/DZgFWCZTtPd7wburnfUWWBm0+vzaJx8VujzoPhzr9DnQfHnXr7NQ5NV4br7FmAGMCpp1ChCy9lUpgFHpig/3d23xpymiIhIgzV1X7i3AA+Z2ZvAVEIr2xLgTgAzexDA3c+Lyt8J/MjMxgJ3AcOA84Gz6jtNERGRxtCkCdTd/2ZmnYFfEq5hvgsc6+6LoiKlSeUXmtmxwK2E21KWAZe6+/gMppkPmrTKuJEU+jwo/twr9HlQ/LmXV/PQZI2IREREdibqC1dERCQGJVAREZEYlECbiJlZrmMQEZHsUQJtIq6LzTmjgxcRaQxqRNSIzKwVMAD4JrAWmA3MBz509w1mZkqsTUe/d+6Y2W7u/nmu49iVJS4DbQvZoQTaiMzsf4CTCU+H6Qh0I9yK8yTwP+7+fu6iqz8za0Y4iS6oHaCZfQkYAZwJrAHmAf8BZrl7ZS5jy4SZNQc+L7TfX/KPmbV39/W5jmNnoQTaSMysL/A6cCoww91XmVkR8B3g+4TuBy8DxuXrkaCZDXL3GUnDmhF25nkZcyIze4CQQOcRDmC6Ap8AbwH3uPvkHIZXJzM7zN1fTRpWUMnUzLoCFwJDgPcJfU+/SziIWZPvZ0KJ8RXab1/NzPoQHvtYRlQDBrwNvOLui6Myeb0c8pUSaCMxs2uAo919RPS+ubtvSxg/hnB2+jV3X5ajMNMyswMIO7s5hMfDPeTuMxPGG6EjjjLg7ahbxbwRHcC8QejW8d/uXmVmuwOnA98jPAbveuAG8vCAwMx6E377z4DngT+7+9SE8Qa0AI4C3nT3dP1J54yZdQfGA22AfwP9Cf1UrwamALfmey1MdNDbx92nJAwzwlOgqvJtvUlmZvsDLxD6Bp8K9CYcvLciJNN73D35cZB5w8y6ELbhF9x9dS3lWrj71qaLLFAjosZTARSbWU8Ad99mZs3NrHU0fhywATgtVwHW4SzCGcM/gKHAc2b2upldaWZdox3HXoSz7L1yGGc6XwfecffXo+TZ0t3XRg9ePwT4IfBdYP883QmeDJQDY4B9gJfN7CMzu8nMqmPeA3gaaJnDOGtzFeFpSUe4+3nufjDhOb2PAMcDr5vZibkMsB5GA/+KfvvbzKyvB9vc3c1sNzMrNbPTotqZfHMF4bLFN9z9anf/JnAS8GdCIn3SzL6TywDr8EvgQWC+mT1uZsdGbUtqmFkpcFny8KagBNp4phCeGvOcmZ1uZq2ijW4ThG4KCUexm3IZZC16EY5c/0A4Y7uGUPV2DjDNzJ4l9E9c4e5LchZleuVAqZmNhPAwg+gApk00/nFgEdv3q5xP9iGcMdwFnAh8DfgL8A1gnpm9AzxK+P0X5yzK2vUDprj7R2bWIqqF+dDdf+/u+wGTgIujJJSvLaWHEPrVvgM4DHjXzOab2TVm1imqzv028Ad3r8ploGnsB7zl7uvNrJmZNXP35e5+X1Q7difwPTNrm+M40xlM2Af9N+EyzJPAQjP7s5kdHJX5HnCxu29u8ujcXa9GehE6tf8bX1SD/oawI/wy8CdgBdAu13GmiLs58C3g6qThnYBDgR8BjwGfA9/Jdbxp5qE14ey5kvCAgTYpyrwN/DDXsaaIqxlwLPCDpOEtCf1FH0eowfgcuCDX8dYyH6Oj37h9wrAW1cuCkJDeB4bmOtY08e8H/J3wAIvdCAc1RwG3Ea4jfk44yFkN/DTX8aaZh8uiWHsnrUcto//7AgsJtQQ5jzcp9hLCge5F0fvmhCroq6L1qgqYRbjMcVkuYtQ10EYWdXT/DUKVYg/CmV1H4GXgLnd/NIfh1Uuq6wtmdjLwBPAld9+Qm8hqF51t/pbQkGsjobr5aeBT4ALCDrxXvsZfLdUtIGZ2FDCR/P79BwHPEp7rO9rdn0ka34tQU9ApH+fBzDoQbkH7wN1fThjehrBzHwT8gLAetXf3jTkJtBbRdegJhOr+37j7X5LG9yc0qtsj35aBmbUjnHB87O5vJI1rS7imfgXhckdOfn8l0EZgZvsCPaO3nxHOQDcSEuiXCNc+V3otF8VzKd09e1ErxCp3dzO7CRjs7oc3eYD1EFVVVUW3shwGDAf+i1AltBuh+vAed38hh2GmFFVnWqplkFBmNHCoux/VZIFloLpVZ9QG4EbCdfSVwKuExN8XOIWQnE7PXaT1U91wyBMaAkbD/wrsk6/bAYRbV4DfAWcTagBeJKz//Qnbxtv+xSMk81aqlsJmdj+hHcPwnMSkBJpdZnYJodn+QEKiXAAsBv4FPOH5e72qRsIBgBGqqea6+0cJ441wXW6pu/87N1FmxsxaAkWEA5nWwFp3/yy3UcVnZocDK9x9dq5jqUvUcO7I6PVfhGujq4B7Ca278+nRg7VKbIFLaF08BfidJzxiMV9Ese4WHUi2Bg4k3Nb1NeBgQtXtw8CExO07X5jZboT7z1Mmqagm4GngDnd/skmDq45BCTR7oura+cDNhEYHRYSdxhGEI+6lhOeZzsnX+66SDgA+I8zPEmAa8LS7z81heHUyszaJVTl1bYT5Jjn+QhT95icS1v82hOucU9x9XbQjd0KV28ochlmrpHloS9h2X3b3jxPKtAKOdPfncxNl5izhPm4z293d1+Y6prjMrAWhFmxazmIokP1KQTCzHwPnuPt/pRh3GKEaZR/gkHzcedRxANCbsBP5SXQA0MzzrNWhmXUkXFN7nnBk/Vp14kxMpBZuLF/iedYjSx3xJ97Q3weodPdPchZsGlF14b2EdeZzwjpjhIOxScBf3X1eVDYvu/dLMQ9LCEl/I6HtwsPu/l7uIqxdlFi6A4s8RcvUfD14r1ZX/PlEt7Fk1xagfXRhHjNrFVUd4qFHmbMJt618PXch1upbwH/c/QZ3X+Xu77n7/3P3Uwi9J7Uh3JazZ74lz8g5QBdC444phHvHrjezXu5efdTdlXAfYlEuA02jtvirk2d1/J1yF2atLiU0lDvW3bsQ1qmbCbdAHQ/cYqFzAvIxeUaS5+FsYCyhL+ujgBur5yFP/RCYCdxpZseb2d6J96hG20EHMzsmSlb5ptb4ITTwMrNvVO9fc0VnoFkUncG9TGjt+dPqMxzbvhPn14Cn3P3G3EWampl9n9Ds/XR3fzeqonKPehmycMPy/wE3uPsjOQw1JTMbR7g29WvCTeJnEVrg9iD0hPMXoDPwC3f/Uq7iTKfQ4wcws1eAJ939lqThzYBhhDO799396FzEVx+FPg9mNo1woN6c0HHFh4T7JycQulBca2YXA+e7+9DcRZpaIcWvM9AsiS7Yryb0nDEKWGZm90ZN+bHQW8k5hAv5j+Uu0lo9Qaiy+omFTqc3e+iAYDcAd/+Q0JfsvrkMMpUo2c8BFrv7x+7+jrtfTWh1e1Q0bjThtpY/5CzQNAo9fqhppf0ucEr1GZp9cfN+lYfu8C4G9jWzgbmMNZ1Cn4co5q2EPraHE+5lvZdw7/AUYLKZXQX8hNDVZV4puPg9D26Y3ZlehPutBhA2sr8T7jn8lHBtcQHhfricx5kiboteJxF66FlPWHEHEQ60SglVjOuBbrmON808tAL2jv5vRmiBmDj+cMIBwr65jnVnjD+KcSih0dAfgC4pxneNtod9ch3rzjgPQDHwU+CoFOPKCJ1ArIrWI8XfwJeqcLPAzPYCziV0N7WS0NjgE+AVwlFSC2B/QkKd53n8o5vZHoRk+RXCTeTDolEfERLpg+4+OjfRpZdw32EP4DNP6Fw9YdyvCdU+PXIXaWqFHj/UNNTajdBJxRhCFdwThN64FhMOLI8ndM4+JFdx1mYnmYc2hEsvm6KaMYgGRON/S7i+W5arGGtTSPErgWaBhZt5+xF6XVlNaOBxIKHLvo+BX3pSTxr5pNAPABLiv5zwe28jdOH3OOEet8+iDfF7wDJ3fy5nwaZQ6PGnEh2InU9oRHQQoeZiM/Am4b7JvN0eqhXyPKRraWuhB5+3gPvcPS8vBUDhxK8E2kDRjm094YhoSsKwUkJV0HcIjUBOd/e3chZoLXaCA4D72TH+MsKtN0uAP3p+P7Lpfgo4fqjp9m594k4vOptrTeh9qz/hzDqf16OCnodU8aco0xo4A/hfz79HEBZc/EqgDWRm/QhPxfieu7+eYnwrQofT//DQKCSvFPoBQC3xdyX0evM9QkOEsxR/4zGzuwhnZm8S7t9bl6JMR8/jh2gX+jzUM/49PA/vH4YCjT/XF2EL/UW4N/KfhJ56DiCp4UdU5seE/iZzHm+K2PoRnmiQ8okYhIYt0wlVVjmPN0b8LRV/o8/DWYRGHZ8QGsrdRejguydfPHnlS8BTwIG5jndnnIc08X+TcOmlOv7qru/65zrenSV+nYFmgZkNJTxXbyNhwf8TWO3h2lVbwgNhN7n7OTkMM6Xogv1zhO7KziPc35b85I8fEx5bdlAOQqyV4s+9hPtXbyQknW8TdnxzCc+U/SehY4I/uXtePvy70OdB8eeGEmiWWOh96FfACYRuy6YRnvd5JKFByHfdfVbuIkyvkA8AQPHnUnTf5JVAB3f/ecLwfoTq51MJ1xD3AB5w9+/kJNBaFPo8KP7cUQLNsqhF5TcI91NuItyU/bjncd+ZUNgHAKD4c8lCH75d3P09C12rbfWEHYuZnQH8L3Cwu7+dqzhrU+jzoPhzQwm0EVmedpZdm0I9AKim+PND1HrVPDxK63uEqre2uY4rE4U+D4q/8SmBSlqFeACQSPHnBzO7nPAw6j/mOpa4Cn0eFH/jUAIVkUZl4YkfVYV8MFDo86D4G4cSqIiISAx6GouIiEgMSqAiIiIxKIGKiIjEoAQqIiISgxKoiIhIDEqgIiIiMfx/6yIsJmF64hMAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get the basis gates for the noise model\n", - "basis_gates = noise_model.basis_gates\n", - "\n", - "# Select the QasmSimulator from the Aer provider\n", - "simulator = Aer.get_backend('qasm_simulator')\n", - "\n", - "# Execute noisy simulation and get counts\n", - "result_noise = execute(circ, simulator, \n", - " noise_model=noise_model,\n", - " coupling_map=coupling_map,\n", - " basis_gates=basis_gates).result()\n", - "counts_noise = result_noise.get_counts(circ)\n", - "plot_histogram(counts_noise, title=\"Counts for 3-qubit GHZ state with depolarizing noise model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comparison to the real device\n", - "\n", - "Now we will execute the circuit on the real device too see the effect of the actual noise processes on the output counts. Note that this execution may take some time to return the results." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Submit job to real device and wait for results\n", - "job_device = execute(circ, device)\n", - "job_monitor(job_device)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get results from completed execution\n", - "result_device = job_device.result()\n", - "counts_device = result_device.get_counts(0)\n", - "plot_histogram(counts_device, title='Counts for 3-qubit GHZ state on IBMQ device: {}'.format(device.name()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might notice that the counts returned in the above histogram don't look exactly like our simulation. This is because the real errors that happen on a device can be very complicated, and characterizing them to create acurate models is an active area of quantum computing research. The *basic device noise model* we used for our simulation is a simplified error model that only takes into account *local* errors occuring the qubits participating in each individual gates, and treates the erors as a relaxation process combined with an error operation which acts to randomize the state of the qubit with some probability *p* (the depolarizing probability). While this model is an approximation, due to its simplicity it is a useful starting point for studying the effects of noise on quantum computations." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (qiskit-dev)", - "language": "python", - "name": "qiskit-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/images/qiskit-heading.gif b/examples/images/qiskit-heading.gif deleted file mode 100644 index 97c168a335d7f242580f6d5dfd0a640655c385cb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4834 zcmcIn`9GBV<9$Cf&unIx)tDJWV@;MCYlIpaOoJLxme9ylD&1^XqU9O8REAWdWe8E! zRJyn=Mx|`kWGO9*+orToh+FqHpL@Um!}t7fem$@AI_HOT{8sq7E{>^&(!e124@4r7 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Wood (cjwood@us.ibm.com)\n", - "* *Last Updated*: December 10, 2018\n", - "\n", - "**TODO: add more text**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Import Terra\n", - "import qiskit\n", - "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n", - "\n", - "# Import Aer\n", - "import qiskit_aer\n", - "from qiskit_aer.backends import QasmSimulator\n", - "\n", - "from qiskit_aer import noise\n", - "from qiskit_aer.noise import NoiseModel, QuantumError, ReadoutError" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Noise Module\n", - "\n", - "This notebook introduces some of the features of the Qiskit Aer noise module and shows how to construct a noise model for noisy simulation on the Qasm Simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on package qiskit_aer.noise in qiskit_aer:\n", - "\n", - "NAME\n", - " qiskit_aer.noise - Noise module for Qiskit Aer.\n", - "\n", - "DESCRIPTION\n", - " This module contains classes and functions to build a noise model for\n", - " simulating a Qiskit quantum circuit in the presence of errors.\n", - " \n", - " Noise Models\n", - " ----------------------\n", - " Noise models for a noisy simulator are represented using the `NoiseModel`\n", - " class. This can be used to generate a custom noise model, or an automatic\n", - " noise model can be generated for a device using built in functions.\n", - " \n", - " Automatically Generated Noise Models\n", - " ------------------------------------\n", - " Approximate noise models for a hardware device can be generated from the\n", - " device properties using the functions from the `device` submodule.\n", - " \n", - " * `device.depolarizing_noise_model`: Generates a noise model based on\n", - " one-qubit depolarizing errors acting after X90 pulses during u1, u2,\n", - " and u3 gates, two-qubit depolarizing errors acting after cx gates,\n", - " and readout errors acting after measurement. The error parameters\n", - " are tuned for each individual qubit based on 1 and 2-qubit error\n", - " parameters from the device backend properties.\n", - " \n", - " * `device.thermal_relaxation_noise_model`: Generates a noise mode\n", - " based on one-qubit thermal relaxation errors sacting after X90\n", - " pulses during u1, u2, and u3 gates, acting on both qubits after cx\n", - " gates, and readout errors acting after measurement. The error\n", - " parameters are tuned for each individual qubit based on the T_1,\n", - " T_2, and single qubit gate time parameters from the device backend\n", - " properties.\n", - " \n", - " Custom Noise Models\n", - " -------------------\n", - " Custom noise models may be constructed by adding errors to a NoiseModel\n", - " object. Errors are represented using by the `QuantumError` and\n", - " `ReadoutError` classes:\n", - " \n", - " * `QuantumErrors`: Errors that affect the quantum state during a\n", - " simulation. They may be applied after specific circuit gates or\n", - " reset operations, or before measure operations of qubits.\n", - " \n", - " * `ReadoutErrors`: Errors that apply to classical bit registers\n", - " after a measurement. They do not change the quantum state of the\n", - " system, only the recorded classical measurement outcome.\n", - " \n", - " Helper functions exist for generating standard quantum error channels in\n", - " the `errors` submodule. These functions are:\n", - " \n", - " * `errors.kraus_error`\n", - " * `errors.mixed_unitary_error`\n", - " * `errors.coherent_unitary_error`\n", - " * `errors.pauli_error`\n", - " * `errors.depolarizing_error`\n", - " * `errors.thermal_relaxation_error`\n", - " * `errors.phase_amplitude_damping_error`\n", - " * `errors.amplitude_damping_error`\n", - " * `errors.phase_damping_error`\n", - "\n", - "PACKAGE CONTENTS\n", - " aernoiseerror\n", - " device (package)\n", - " errors (package)\n", - " ibmq (package)\n", - " noise_model\n", - " noise_utils\n", - " quantum_error\n", - " readout_error\n", - "\n", - "FILE\n", - " /Users/cjwood/anaconda/envs/qiskit-dev/lib/python3.6/site-packages/qiskit_aer/noise/__init__.py\n", - "\n", - "\n" - ] - } - ], - "source": [ - "help(noise)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test Circuit" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Test circuit\n", - "qr = QuantumRegister(2)\n", - "cr = ClassicalRegister(2)\n", - "circ = QuantumCircuit(qr, cr)\n", - "circ.h(qr[0])\n", - "circ.cx(qr[0], qr[1])\n", - "circ.measure(qr, cr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ideal Simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'00': 532, '11': 468}\n" - ] - } - ], - "source": [ - "# Ideal Backend\n", - "backend = QasmSimulator()\n", - "job = qiskit.execute(circ, backend, shots=1000)\n", - "result = job.result()\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Depolarizing Channel Noise" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 1 qubits. Noise circuits:\n", - " P(0) = 0.25, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.25, QobjInstructions = [[{'name': 'y', 'qubits': [0]}]\n", - " P(2) = 0.25, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(3) = 0.25, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "QuantumError on 2 qubits. Noise circuits:\n", - " P(0) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}]\n", - " P(2) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(3) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [1]}]\n", - " P(4) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(5) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(6) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(7) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [1]}]\n", - " P(8) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(9) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(10) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(11) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [1]}]\n", - " P(12) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(13) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(14) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(15) = 0.0625, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['cx', 'h']\n", - " All-qubits errors: ['h']\n", - " Specific qubit errors: [('cx', [0, 1])]\n", - "\n", - "Counts:\n", - "{'01': 258, '00': 225, '10': 263, '11': 254}\n" - ] - } - ], - "source": [ - "# Errors\n", - "error1 = depolarizing_error(1, 1, standard_gates=True)\n", - "print(error1, \"\\n\")\n", - "error2 = depolarizing_error(1, 2, standard_gates=True)\n", - "print(error2, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_all_qubit_quantum_error(error1, \"h\")\n", - "noise_model.add_quantum_error(error2, \"cx\", [0, 1])\n", - "print(noise_model)\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "print(\"\\nCounts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### X-90 based deplarizing error model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 1 qubits. Noise circuits:\n", - " P(0) = 0.25, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.25, QobjInstructions = [[{'name': 'y', 'qubits': [0]}]\n", - " P(2) = 0.25, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(3) = 0.25, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "QuantumError on 2 qubits. Noise circuits:\n", - " P(0) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}]\n", - " P(2) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(3) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [1]}]\n", - " P(4) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(5) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(6) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(7) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [1]}]\n", - " P(8) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(9) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(10) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(11) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [1]}]\n", - " P(12) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(13) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(14) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(15) = 0.0625, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['cx', 'x90', 'h']\n", - " X-90 based single qubit gates: ['h']\n", - " All-qubits errors: ['x90']\n", - " Specific qubit errors: [('cx', [0, 1])]\n", - "\n", - "Counts:\n", - "{'01': 231, '00': 284, '10': 250, '11': 235}\n" - ] - } - ], - "source": [ - "# Errors\n", - "error1 = depolarizing_error(1, 1, standard_gates=True)\n", - "print(error1, \"\\n\")\n", - "error2 = depolarizing_error(1, 2, standard_gates=True)\n", - "print(error2, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.set_x90_single_qubit_gates(['h'])\n", - "noise_model.add_all_qubit_quantum_error(error1, \"x90\")\n", - "noise_model.add_quantum_error(error2, \"cx\", [0, 1])\n", - "\n", - "print(noise_model)\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "print(\"\\nCounts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Depolarizing noise from Kraus matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 1 qubits. Noise circuits:\n", - " P(0) = 0.25, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.25, QobjInstructions = [[{'name': 'y', 'qubits': [0]}]\n", - " P(2) = 0.25, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(3) = 0.25, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "QuantumError on 2 qubits. Noise circuits:\n", - " P(0) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}]\n", - " P(2) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(3) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [1]}]\n", - " P(4) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(5) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(6) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}]\n", - " P(7) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [1]}]\n", - " P(8) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(9) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(10) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}]\n", - " P(11) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [1]}]\n", - " P(12) = 0.0625, QobjInstructions = [[{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(13) = 0.0625, QobjInstructions = [[{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(14) = 0.0625, QobjInstructions = [[{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}]\n", - " P(15) = 0.0625, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['cx', 'h']\n", - " All-qubits errors: ['h', 'cx'] \n", - "\n", - "Counts:\n", - "{'01': 255, '00': 260, '10': 232, '11': 253}\n" - ] - } - ], - "source": [ - "# We define a unitary channel as a Kraus set\n", - "# Note that internally this will be converted to a mixed unitary error channel\n", - "\n", - "I = np.eye(2, dtype=complex)\n", - "X = np.array([[0, 1], [1, 0]], dtype=complex)\n", - "Y = np.array([[0, -1j], [1j, 0]], dtype=complex)\n", - "Z = np.array([[1, 0], [0, -1]], dtype=complex)\n", - "\n", - "# Errors\n", - "kraus_ops1 = [np.sqrt(0.25) * I, np.sqrt(0.25) * X, np.sqrt(0.25) * Y, np.sqrt(0.25) * Z]\n", - "error1 = kraus_error(kraus_ops1)\n", - "print(error1, \"\\n\")\n", - "\n", - "kraus_ops2 = [np.kron(a,b) for a in kraus_ops1 for b in kraus_ops1]\n", - "error2 = kraus_error(kraus_ops2)\n", - "print(error2, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_all_qubit_quantum_error(error1, \"h\")\n", - "noise_model.add_all_qubit_quantum_error(error2, \"cx\")\n", - "print(noise_model, \"\\n\")\n", - "\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "\n", - "print(\"Counts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Classical readout error" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ReadoutError on 1 qubits. Assignment probabilities:\n", - " P(j|0) = [0.9, 0.1]\n", - " P(j|1) = [0.3, 0.7] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['measure']\n", - " All-qubits errors: ['measure'] \n", - "\n", - "Counts:\n", - "{'01': 137, '00': 488, '10': 142, '11': 233}\n" - ] - } - ], - "source": [ - "# Error\n", - "p_given_0 = [0.9, 0.1]\n", - "p_given_1 = [0.3, 0.7]\n", - "error = ReadoutError([p_given_0, p_given_1])\n", - "print(error, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_all_qubit_readout_error(error)\n", - "print(noise_model, \"\\n\")\n", - "\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "print(\"Counts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Amplitude Damping Error" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 1 qubits. Noise circuits:\n", - " P(0) = 1.0, QobjInstructions = [[{'name': 'kraus', 'qubits': [0], 'params': [array([[ 1. +0.j, -0. +0.j],\n", - " [-0. +0.j, 0.70710678+0.j]]), array([[0. +0.j, 0.70710678+0.j],\n", - " [0. +0.j, 0. +0.j]])]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['h']\n", - " All-qubits errors: ['h'] \n", - "\n", - "Counts:\n", - "{'00': 761, '11': 239}\n" - ] - } - ], - "source": [ - "# Error\n", - "error1 = amplitude_damping_error(0.5)\n", - "print(error1, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_all_qubit_quantum_error(error1, \"h\")\n", - "print(noise_model, \"\\n\")\n", - "\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "\n", - "# Results\n", - "print(\"Counts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase damping error" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 1 qubits. Noise circuits:\n", - " P(0) = 0.066987298108, QobjInstructions = [[{'name': 'z', 'qubits': [0]}]\n", - " P(1) = 0.933012701892, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['h']\n", - " All-qubits errors: ['h'] \n", - "\n", - "Counts:\n", - "{'00': 492, '11': 508}\n" - ] - } - ], - "source": [ - "error1 = phase_damping_error(0.25)\n", - "print(error1, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_all_qubit_quantum_error(error1, \"h\")\n", - "print(noise_model, \"\\n\")\n", - "\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "\n", - "# Counts\n", - "print(\"Counts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pauli Error (matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 2 qubits. Noise circuits:\n", - " P(0) = 0.5, QobjInstructions = [[{'name': 'x', 'qubits': [0]}]\n", - " P(1) = 0.5, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['cx']\n", - " Specific qubit errors: [('cx', [0, 1])]\n", - "\n", - "Counts:\n", - "{'01': 293, '00': 231, '10': 232, '11': 244}\n" - ] - } - ], - "source": [ - "# Errors\n", - "error2 = pauli_error([('II', 0.5), ('IX', 0.5)], standard_gates=True)\n", - "print(error2, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_quantum_error(error2, \"cx\", [0, 1])\n", - "print(noise_model)\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "print(\"\\nCounts:\")\n", - "print(result.get_counts(circ))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pauli Error (gates)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QuantumError on 2 qubits. Noise circuits:\n", - " P(0) = 0.5, QobjInstructions = [[{'name': 'unitary', 'qubits': [0, 1], 'params': array([[ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],\n", - " [ 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", - " [ 0.+0.j, 0.+0.j, -0.+0.j, -1.+0.j],\n", - " [ 0.+0.j, 0.+0.j, -1.+0.j, -0.+0.j]])}]\n", - " P(1) = 0.5, QobjInstructions = [[{'name': 'id', 'qubits': [0]}] \n", - "\n", - "NoiseModel:\n", - " Instructions with noise: ['cx']\n", - " Specific qubit errors: [('cx', [0, 1])]\n", - "\n", - "Counts:\n", - "{'01': 275, '00': 220, '10': 241, '11': 264}\n" - ] - } - ], - "source": [ - "# Errors\n", - "error2 = pauli_error([('II', 0.5), ('ZX', 0.5)], standard_gates=False)\n", - "print(error2, \"\\n\")\n", - "\n", - "# Noise Model\n", - "noise_model = NoiseModel()\n", - "noise_model.add_quantum_error(error2, \"cx\", [0, 1])\n", - "print(noise_model)\n", - "# Backend\n", - "backend = QasmSimulator()\n", - "qobj = qiskit.compile(circ, backend, shots=1000,\n", - " basis_gates=noise_model.basis_gates)\n", - "job = backend.run(qobj, noise_model=noise_model)\n", - "result = job.result()\n", - "print(\"\\nCounts:\")\n", - "print(result.get_counts(circ))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (qiskit-dev)", - "language": "python", - "name": "qiskit-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 37dda57457330e688008da0f2130f6d1134b56d2 Mon Sep 17 00:00:00 2001 From: cjwood Date: Mon, 17 Dec 2018 16:46:01 -0500 Subject: [PATCH 2/2] remove old qobj examples, update cpp test qobj --- .../qobj_snapshot_expval_matrix.json} | 8 +- test/data/qobj_snapshot_expval_pauli.json | 48 + test/data/qobj_snapshot_matrix.json | 44 - test/data/qobj_snapshot_pauli.json | 33 - test/data/qobj_snapshot_probs.json | 7 +- ...te.json => qobj_snapshot_statevector.json} | 6 +- test/qobj/noise_depol.json | 35 - test/qobj/noise_kraus.json | 34 - test/qobj/noise_reset.json | 87 - test/qobj/qobj_depol_noise_kraus.json | 47 - test/qobj/quantum_volume_10q_10d_0s.json | 6967 -------- test/qobj/quantum_volume_20q_10d_0s.json | 13887 ---------------- test/qobj/snapshot_pauli.json | 61 - test/qobj/snapshot_probs.json | 23 - test/qobj/snapshot_state.json | 21 - test/src/test_snapshot.cpp | 6 +- test/src/test_snapshot_bdd.cpp | 6 +- 17 files changed, 65 insertions(+), 21255 deletions(-) rename test/{qobj/snapshot_matrix.json => data/qobj_snapshot_expval_matrix.json} (88%) create mode 100644 test/data/qobj_snapshot_expval_pauli.json delete mode 100644 test/data/qobj_snapshot_matrix.json delete mode 100644 test/data/qobj_snapshot_pauli.json rename test/data/{qobj_snapshot_state.json => qobj_snapshot_statevector.json} (63%) delete mode 100644 test/qobj/noise_depol.json delete mode 100644 test/qobj/noise_kraus.json delete mode 100644 test/qobj/noise_reset.json delete mode 100644 test/qobj/qobj_depol_noise_kraus.json delete mode 100644 test/qobj/quantum_volume_10q_10d_0s.json delete mode 100644 test/qobj/quantum_volume_20q_10d_0s.json delete mode 100644 test/qobj/snapshot_pauli.json delete mode 100644 test/qobj/snapshot_probs.json delete mode 100644 test/qobj/snapshot_state.json diff --git a/test/qobj/snapshot_matrix.json b/test/data/qobj_snapshot_expval_matrix.json similarity index 88% rename from test/qobj/snapshot_matrix.json rename to test/data/qobj_snapshot_expval_matrix.json index e36d3e68ee..0b93c534f7 100644 --- a/test/qobj/snapshot_matrix.json +++ b/test/data/qobj_snapshot_expval_matrix.json @@ -14,7 +14,7 @@ {"name": "cx", "qubits": [0, 1]}, { "name": "snapshot", - "type": "expval_matrix", + "type": "expectation_value_matrix", "label": "pre_measure", "params": [ [1, [[[0], [[1, 0], [0, -1]]], @@ -24,7 +24,7 @@ }, { "name": "snapshot", - "type": "expval_matrix", + "type": "expectation_value_matrix", "label": "pre_measure", "params": [ [1, [[[0], [[1, 0], [0, -1]]]]], @@ -34,7 +34,7 @@ {"name": "measure", "qubits": [0, 1], "memory": [0, 1]}, { "name": "snapshot", - "type": "expval_matrix", + "type": "expectation_value_matrix", "label": "post_measure", "params": [ [1, [[[0], [[1, 0], [0, -1]]], @@ -44,7 +44,7 @@ }, { "name": "snapshot", - "type": "expval_matrix", + "type": "expectation_value_matrix", "label": "post_measure", "params": [ [1, [[[0], [[1, 0], [0, -1]]]]], diff --git a/test/data/qobj_snapshot_expval_pauli.json b/test/data/qobj_snapshot_expval_pauli.json new file mode 100644 index 0000000000..ef0c1fb5f5 --- /dev/null +++ b/test/data/qobj_snapshot_expval_pauli.json @@ -0,0 +1,48 @@ +{ + "qobj_id": "pauli_observable_snapshot_example", + "schema_version": "1.0.0", + "type": "QASM", + "experiments": [ + { + "config": { + "shots": 1000, + "memory_slots": 2, + "n_qubits": 2 + }, + "instructions": [ + {"name": "h", "qubits": [0]}, + {"name": "cx", "qubits": [0, 1]}, + { + "name": "snapshot", + "type": "expectation_value_pauli", + "label": "pre_measure", + "qubits": [0, 1], + "params": [[[1, 0] , "ZZ"]] + }, + { + "name": "snapshot", + "type": "expectation_value_pauli", + "label": "pre_measure", + "qubits": [0, 1], + "params": [[[1, 0] , "ZI"], [[1, 0], "IZ"]] + }, + {"name": "measure", "qubits": [0], "memory": [0]}, + {"name": "measure", "qubits": [0], "memory": [1]}, + { + "name": "snapshot", + "type": "expectation_value_pauli", + "label": "post_measure", + "qubits": [0, 1], + "params": [[[1, 0] , "ZI"], [[1, 0], "ZZ"]] + }, + { + "name": "snapshot", + "type": "expectation_value_pauli", + "label": "post_measure", + "qubits": [0, 1], + "params": [[[1, 0] , "ZI"], [[1, 0], "IZ"]] + } + ] + } + ] +} \ No newline at end of file diff --git a/test/data/qobj_snapshot_matrix.json b/test/data/qobj_snapshot_matrix.json deleted file mode 100644 index e79b4f44a6..0000000000 --- a/test/data/qobj_snapshot_matrix.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "qobj_id": "matrix_observable_snapshot_example", - "type": "QASM", - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 2, - "n_qubits": 2 - }, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "cx", "qubits": [0, 1]}, - {"name": "snapshot", "type": "matrix_observable", - "label": "pre_measure", - "params": [{"coeff": 1, "qubits": [[1], [0]], "ops": [ - [[[1, 0], [0, 0]], [[0, 0], [0, -1]]], - [[[1, 0], [0, 0]], [[1, 0], [0, -1]]] - ]}]}, - {"name": "snapshot", "type": "matrix_observable", - "label": "pre_measure", - "params": [{"coeff": 1, "qubits": [[1]], "ops": [ - [[[1, 0], [0, 0]], [[0, 0], [0, -1]]]]}, - {"coeff": 1, "qubits": [[0]], "ops": [ - [[[1, 0], [0, 0]], [[0, 0], [0, -1]]]]} - ]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]}, - {"name": "snapshot", "type": "matrix_observable", - "label": "post_measure", - "params": [{"coeff": 1, "qubits": [[1], [0]], "ops": [ - [[[1, 0], [0, 0]], [[0, 0], [0, -1]]], - [[[1, 0], [0, 0]], [[1, 0], [0, -1]]] - ]}]}, - {"name": "snapshot", "type": "matrix_observable", - "label": "post_measure", - "params": [{"coeff": 1, "qubits": [[1]], "ops": [ - [[[1, 0], [0, 0]], [[0, 0], [0, -1]]]]}, - {"coeff": 1, "qubits": [[0]], "ops": [ - [[[1, 0], [0, 0]], [[0, 0], [0, -1]]]]} - ]} - ] - } - ] -} \ No newline at end of file diff --git a/test/data/qobj_snapshot_pauli.json b/test/data/qobj_snapshot_pauli.json deleted file mode 100644 index adbfb56bb6..0000000000 --- a/test/data/qobj_snapshot_pauli.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "qobj_id": "pauli_observable_snapshot_example", - "schema_version": "1.0.0", - "type": "QASM", - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 2, - "n_qubits": 2 - }, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "cx", "qubits": [0, 1]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "pre_measure", - "params": [{"coeff": 1, "qubits": [1, 0], "op": "ZZ"}]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "pre_measure", - "params": [{"coeff": 1, "qubits": [1, 0], "op": "ZI"}, - {"coeff": 1, "qubits": [1, 0], "op": "IZ"}]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "post_measure", - "params": [{"coeff": 1, "qubits": [1, 0], "op": "ZZ"}]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "post_measure", - "params": [{"coeff": 1, "qubits": [1, 0], "op": "ZI"}, - {"coeff": 1, "qubits": [1, 0], "op": "IZ"}]} - ] - } - ] -} \ No newline at end of file diff --git a/test/data/qobj_snapshot_probs.json b/test/data/qobj_snapshot_probs.json index 8a6acdca2e..03faab580a 100644 --- a/test/data/qobj_snapshot_probs.json +++ b/test/data/qobj_snapshot_probs.json @@ -13,10 +13,11 @@ {"name": "h", "qubits": [0]}, {"name": "cx", "qubits": [0, 1]}, {"name": "snapshot", "type": "probabilities", - "label": "pre_measure", "params": [0, 1]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]}, + "label": "pre_measure", "qubits": [0, 1]}, + {"name": "measure", "qubits": [0], "memory": [0]}, + {"name": "measure", "qubits": [1], "memory": [1]}, {"name": "snapshot", "type": "probabilities", - "label": "post_measure", "params": [0, 1]} + "label": "post_measure", "qubits": [0, 1]} ] } ] diff --git a/test/data/qobj_snapshot_state.json b/test/data/qobj_snapshot_statevector.json similarity index 63% rename from test/data/qobj_snapshot_state.json rename to test/data/qobj_snapshot_statevector.json index 4786c3a94a..1164a7d0ff 100644 --- a/test/data/qobj_snapshot_state.json +++ b/test/data/qobj_snapshot_statevector.json @@ -10,11 +10,11 @@ "n_qubits": 2 }, "instructions": [ - {"name": "snapshot", "type": "state", "label": "initial"}, + {"name": "snapshot", "type": "statevector", "label": "initial"}, {"name": "h", "qubits": [0]}, - {"name": "snapshot", "type": "state", "label": "middle"}, + {"name": "snapshot", "type": "statevector", "label": "middle"}, {"name": "cx", "qubits": [0, 1]}, - {"name": "snapshot", "type": "state", "label": "final"} + {"name": "snapshot", "type": "statevector", "label": "final"} ] } ] diff --git a/test/qobj/noise_depol.json b/test/qobj/noise_depol.json deleted file mode 100644 index fd79f1cc6e..0000000000 --- a/test/qobj/noise_depol.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "qobj_id": "complextely_depolarizing_channel_example", - "schema_version": "1.0.0", - "type": "QASM", - "config": { - "noise_model": { - "errors": [ - { - "type": "qerror", - "operations": ["x"], - "probabilities": [0.7, 0.1, 0.1, 0.1], - "instructions": [ - [{"name": "id", "qubits": [0]}], - [{"name": "x", "qubits": [0]}], - [{"name": "y", "qubits": [0]}], - [{"name": "z", "qubits": [0]}] - ] - } - ] - } - }, - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 1, - "n_qubits": 1 - }, - "instructions": [ - {"name": "x", "qubits": [0]}, - {"name": "measure", "qubits": [0], "memory": [0]} - ] - } - ] -} \ No newline at end of file diff --git a/test/qobj/noise_kraus.json b/test/qobj/noise_kraus.json deleted file mode 100644 index 5b1be5cecd..0000000000 --- a/test/qobj/noise_kraus.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "qobj_id": "amplitude_damping_example", - "schema_version": "1.0.0", - "type": "QASM", - "config": { - "noise_model": { - "errors": [ - { - "type": "qerror", - "operations": ["h"], - "probabilities": [1.0], - "instructions": [ - [{"name": "kraus", "qubits": [0], "params": [ - [[[1, 0], [0, 0]], [[0, 0], [0.5, 0]]], - [[[0, 0], [0.86602540378, 0]], [[0, 0], [0, 0]]]]}] - ] - } - ] - } - }, - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 1, - "n_qubits": 1 - }, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "measure", "qubits": [0], "memory": [0]} - ] - } - ] -} \ No newline at end of file diff --git a/test/qobj/noise_reset.json b/test/qobj/noise_reset.json deleted file mode 100644 index 66e41fd416..0000000000 --- a/test/qobj/noise_reset.json +++ /dev/null @@ -1,87 +0,0 @@ -{ - "qobj_id": "reset_noise_example", - "schema_version": "1.0.0", - "type": "QASM", - "config": { - "noise_model": { - "errors": [ - { - "type": "qerror", - "operations": ["h"], - "gate_qubits": [[0]], - "probabilities": [1], - "instructions": [[{"name": "reset", "qubits": [0]}]] - }, - { - "type": "qerror", - "operations": ["h"], - "gate_qubits": [[1]], - "probabilities": [1], - "instructions": [[{"name": "reset", "qubits": [0]}, - {"name": "x", "qubits": [0]}]] - }, - { - "type": "qerror", - "operations": ["x"], - "probabilities": [0.5, 0.25, 0.25], - "instructions": [ - [{"name": "id", "qubits": [0]}], - [{"name": "reset", "qubits": [0]}], - [{"name": "reset", "qubits": [0]}, {"name": "x", "qubits": [0]}] - ] - }, - { - "type": "qerror", - "operations": ["y"], - "probabilities": [0.25, 0.5, 0.25], - "instructions": [ - [{"name": "id", "qubits": [0]}], - [{"name": "reset", "qubits": [0]}], - [{"name": "reset", "qubits": [0]}, {"name": "x", "qubits": [0]}] - ] - } - ] - } - }, - "experiments": [ - { - "config": { - "shots": 2000, - "memory_slots": 2, - "n_qubits": 2 - }, - "header": {"expected_output": {"0x2": 2000}}, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "h", "qubits": [1]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]} - ] - }, - { - "config": { - "shots": 2000, - "memory_slots": 2, - "n_qubits": 2 - }, - "header": {"expected_output": {"0x0": 125, "0x1": 375, "0x2": 375, "0x3": 1125}}, - "instructions": [ - {"name": "x", "qubits": [0]}, - {"name": "x", "qubits": [1]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]} - ] - }, - { - "config": { - "shots": 2000, - "memory_slots": 2, - "n_qubits": 2 - }, - "header": {"expected_output": {"0x0": 500, "0x1": 500, "0x2": 500, "0x3": 500}}, - "instructions": [ - {"name": "y", "qubits": [0]}, - {"name": "y", "qubits": [1]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]} - ] - } - ] -} \ No newline at end of file diff --git a/test/qobj/qobj_depol_noise_kraus.json b/test/qobj/qobj_depol_noise_kraus.json deleted file mode 100644 index 1820310f47..0000000000 --- a/test/qobj/qobj_depol_noise_kraus.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "qobj_id": "pauli_observable_snapshot_example", - "type": "QASM", - "config": { - "noise_model": { - "errors": [ - { - "type": "kraus", - "qubits": [0], - "operations": ["h"], - "params": [ - [[[0.5, 0], [0, 0]], [[0, 0], [0.5, 0]]], - [[[0, 0], [0.5, 0]], [[0.5, 0], [0, 0]]], - [[[0, 0], [0, -0.5]], [[0, 0.5], [0, 0]]], - [[[0.5, 0], [0, 0]], [[0, 0], [-0.5, 0]]] - ] - } - ] - } - }, - "experiments": [ - { - "config": {"shots": 1000}, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "_1_gate", - "params": [{"coeff": 1, "qubits": [0], "op": "X"}]}, - {"name": "h", "qubits": [0]}, - {"name": "h", "qubits": [0]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "_2_gates", - "params": [{"coeff": 1, "qubits": [0], "op": "X"}]}, - {"name": "h", "qubits": [0]}, - {"name": "h", "qubits": [0]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "_3_gates", - "params": [{"coeff": 1, "qubits": [0], "op": "X"}]}, - {"name": "h", "qubits": [0]}, - {"name": "h", "qubits": [0]}, - {"name": "snapshot", "type": "pauli_observable", - "label": "_4_gates", - "params": [{"coeff": 1, "qubits": [0], "op": "X"}]} - ] - } - ] -} \ No newline at end of file diff --git a/test/qobj/quantum_volume_10q_10d_0s.json b/test/qobj/quantum_volume_10q_10d_0s.json deleted file mode 100644 index 75d34697b9..0000000000 --- a/test/qobj/quantum_volume_10q_10d_0s.json +++ /dev/null @@ -1,6967 +0,0 @@ -{ - "config": { - "max_credits": 10, - "memory_slots": 10, - "n_qubits": 10, - "shots": 10, - "seed": 12345 - }, - "experiments": [ - { - "config": { - "basis_gates": "u0,u1,u2,u3,cx,cz,id,x,y,z,h,s,sdg,t,tdg,rzz,snapshot,wait,noise,save,load", - "coupling_map": null, - "layout": null, - "memory_slots": 10, - "n_qubits": 10 - }, - "header": { - "clbit_labels": [ - [ - "c0", - 10 - ] - ], - "compiled_circuit_qasm": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q0[10];\ncreg c0[10];\nu3(1.90379702174043,2.25552459851131,-2.61958415048729) q0[7];\nu3(2.44588572961775,1.44386538566153,-0.811575063117083) q0[2];\ncx q0[2],q0[7];\nu1(1.96600675038962) q0[7];\nu3(-2.51633632617129,0,0) q0[2];\ncx q0[7],q0[2];\nu3(0.949213077849070,0,0) q0[2];\ncx q0[2],q0[7];\nu3(1.84299487965198,-2.06656416762779,0.353921842539353) q0[7];\nu3(2.56342611843107,-3.88803325859087,-1.86515405605965) q0[2];\nu3(1.79814736396514,-0.0775577529630302,1.93408529580331) q0[5];\nu3(1.60851176666670,-3.05504792861002,-3.00924388607171) q0[3];\ncx q0[3],q0[5];\nu1(1.30107344874904) q0[5];\nu3(-0.114615083165629,0,0) q0[3];\ncx q0[5],q0[3];\nu3(1.67003178404666,0,0) q0[3];\ncx q0[3],q0[5];\nu3(1.44156586363148,-3.55634358607295,1.24099447374204) q0[5];\nu3(2.05480949556862,-0.545545122137092,-2.82485245379511) 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q0[10];\ncx q0[2],q0[10];\nu3(1.04874643923588,0,0) q0[10];\ncx q0[10],q0[2];\nu3(1.51856246350229,-0.560752186167158,2.01435449757711) q0[2];\nu3(1.60440374904904,3.90065813911191,-1.53446534657055) q0[10];\nu3(1.94565878622864,-1.34276966527387,-1.49343844509737) q0[7];\nu3(2.30127155249919,1.62228005139864,-4.54893094663641) q0[11];\ncx q0[11],q0[7];\nu1(2.19341542992023) q0[7];\nu3(-1.83467428114948,0,0) q0[11];\ncx q0[7],q0[11];\nu3(-0.266795868038035,0,0) q0[11];\ncx q0[11],q0[7];\nu3(0.847593228603513,-0.573398971136619,2.11278012219505) q0[7];\nu3(2.02313021783861,0.809844590143230,-0.371547276413012) q0[11];\nu3(1.92555850840215,2.73383156460377,-1.83136392783242) q0[5];\nu3(1.05403650225213,1.27408480982716,-2.32985574372923) q0[9];\ncx q0[9],q0[5];\nu1(2.36236798863783) q0[5];\nu3(-2.61578543736247,0,0) q0[9];\ncx q0[5],q0[9];\nu3(0.446944832741932,0,0) q0[9];\ncx q0[9],q0[5];\nu3(2.55367520474172,1.38160389576809,-4.22100626088611) q0[5];\nu3(1.20922572896057,5.58269097264386,-0.251624624517830) q0[9];\nu3(1.17985252771974,2.43735405725408,0.417698312827382) q0[3];\nu3(0.793468698014634,0.858176627345685,-3.89493890165484) q0[13];\ncx q0[13],q0[3];\nu1(1.65699288697438) q0[3];\nu3(-2.16761618903380,0,0) q0[13];\ncx q0[3],q0[13];\nu3(3.80905640008268,0,0) q0[13];\ncx q0[13],q0[3];\nu3(1.15630050146192,2.71484093876923,-0.791923775089040) q0[3];\nu3(0.849125502899881,4.63012597104862,-1.59308641555535) q0[13];\nu3(2.87658911511675,1.84406295744607,-1.47854521730351) q0[1];\nu3(2.14578815807930,0.0783146345094654,-3.48225305134152) q0[10];\ncx q0[10],q0[1];\nu1(1.33831696974331) q0[1];\nu3(-3.19347491866463,0,0) q0[10];\ncx q0[1],q0[10];\nu3(2.30158529193926,0,0) q0[10];\ncx q0[10],q0[1];\nu3(1.38517483974913,-1.66651253333182,1.77964434425949) q0[1];\nu3(0.821285148425475,-0.793566533096539,-5.34884327930316) q0[10];\nu3(2.25645681648944,0.619874836606920,2.47553684993462) q0[12];\nu3(1.40624421883175,-1.07165788041862,-1.14643843456549) q0[9];\ncx q0[9],q0[12];\nu1(0.442625394166673) q0[12];\nu3(-1.12432569140689,0,0) q0[9];\ncx q0[12],q0[9];\nu3(1.82853120193258,0,0) q0[9];\ncx q0[9],q0[12];\nu3(2.47664487952732,2.21738303270475,-0.439854811967181) q0[12];\nu3(2.72702364917024,-1.92553359637721,-2.63854897024542) q0[9];\nu3(1.99852361944502,3.22258227556662,-2.37462834545201) q0[11];\nu3(1.56379640379744,1.84736252860879,-1.96139865710516) q0[2];\ncx q0[2],q0[11];\nu1(2.37258735096874) q0[11];\nu3(0.206438251784296,0,0) q0[2];\ncx q0[11],q0[2];\nu3(1.17382349664403,0,0) q0[2];\ncx q0[2],q0[11];\nu3(1.60923278286157,-1.19294046130175,3.87058303812528) q0[11];\nu3(0.543606916075238,0.802951290957743,4.45543328123618) q0[2];\nu3(1.22607743058434,-2.07559178441237,0.807985710771828) q0[5];\nu3(1.24879858640135,-4.30895795678609,0.329271762762593) q0[14];\ncx q0[14],q0[5];\nu1(2.87474863796391) q0[5];\nu3(-1.54057524658197,0,0) q0[14];\ncx q0[5],q0[14];\nu3(1.22859937922511,0,0) q0[14];\ncx q0[14],q0[5];\nu3(1.41355727813479,3.70673489056137,-0.863162140198313) q0[5];\nu3(1.48829379751513,-3.15253570153314,0.667674378034478) q0[14];\nu3(1.39841556541994,1.45121049222977,-3.97102200355121) q0[19];\nu3(1.98669565682874,2.15770151112543,-3.54047040373146) q0[16];\ncx q0[16],q0[19];\nu1(-0.519769110150422) q0[19];\nu3(0.822703949363513,0,0) q0[16];\ncx q0[19],q0[16];\nu3(4.22485467004663,0,0) q0[16];\ncx q0[16],q0[19];\nu3(1.88114498647391,2.21679087370122,0.568479282215348) q0[19];\nu3(0.553065008513920,1.95795329021146,-3.85083569599538) q0[16];\nu3(2.12183720672107,-2.59137248107250,-0.483915270643042) q0[0];\nu3(2.40693705760896,0.0782510569611119,0.922189923309547) q0[18];\ncx q0[18],q0[0];\nu1(1.44808134355593) q0[0];\nu3(-1.23761767108729,0,0) q0[18];\ncx q0[0],q0[18];\nu3(2.68943553510272,0,0) q0[18];\ncx q0[18],q0[0];\nu3(1.98591912161734,-2.52982989756827,2.62508124548710) q0[0];\nu3(0.449054187200140,0.142187587893698,-1.57349625295189) q0[18];\nu3(2.47077486597218,-1.42995822890531,-0.0873888506677545) q0[6];\nu3(1.52862672425803,-3.16423984075354,-1.39955757713064) q0[13];\ncx q0[13],q0[6];\nu1(3.67174585518282) q0[6];\nu3(-1.53389522437475,0,0) q0[13];\ncx q0[6],q0[13];\nu3(2.20965343690288,0,0) q0[13];\ncx q0[13],q0[6];\nu3(1.28681903715273,-2.08053428244802,1.57241522529263) q0[6];\nu3(0.891282772012783,-0.479930588063957,4.65385918893792) q0[13];\nu3(1.09946474263056,-2.88523465784646,1.52168550580330) q0[8];\nu3(0.202675610901208,-2.71186593838279,1.39265832754271) q0[4];\ncx q0[4],q0[8];\nu1(2.05760821472405) q0[8];\nu3(-2.83859835920035,0,0) q0[4];\ncx q0[8],q0[4];\nu3(0.549501301801189,0,0) q0[4];\ncx q0[4],q0[8];\nu3(0.235106774374916,2.71645436901784,-0.142035949839210) q0[8];\nu3(1.65223233999093,-4.23184321988084,-0.931126742294628) q0[4];\nu3(1.37773372451652,2.53785917187148,-1.18475415647400) q0[17];\nu3(1.23616702717167,1.46204172860687,-2.45963787379796) q0[3];\ncx q0[3],q0[17];\nu1(-0.341719374757645) q0[17];\nu3(-1.97772910908239,0,0) q0[3];\ncx q0[17],q0[3];\nu3(0.814334897636308,0,0) q0[3];\ncx q0[3],q0[17];\nu3(2.48648886940739,1.11197596468097,-1.34793757814583) q0[17];\nu3(1.79314642267384,0.632400029265770,1.21969988038359) q0[3];\nu3(3.00525427773829,1.26828811261936,-1.17957402455897) q0[7];\nu3(2.92083572561166,0.812659512253307,-3.78820789908841) q0[15];\ncx q0[15],q0[7];\nu1(2.05196674551245) q0[7];\nu3(0.583091342874369,0,0) q0[15];\ncx q0[7],q0[15];\nu3(1.37059152368556,0,0) q0[15];\ncx q0[15],q0[7];\nu3(1.23135958153321,-2.22491612930457,1.95642058352796) q0[7];\nu3(2.21252297740219,0.0549653723255998,-4.18904340349148) q0[15];\nbarrier q0[0],q0[1],q0[2],q0[3],q0[4],q0[5],q0[6],q0[7],q0[8],q0[9],q0[10],q0[11],q0[12],q0[13],q0[14],q0[15],q0[16],q0[17],q0[18],q0[19];\nmeasure q0[0] -> c0[0];\nmeasure q0[1] -> c0[1];\nmeasure q0[2] -> c0[2];\nmeasure q0[3] -> c0[3];\nmeasure q0[4] -> c0[4];\nmeasure q0[5] -> c0[5];\nmeasure q0[6] -> c0[6];\nmeasure q0[7] -> c0[7];\nmeasure q0[8] -> c0[8];\nmeasure q0[9] -> c0[9];\nmeasure q0[10] -> c0[10];\nmeasure q0[11] -> c0[11];\nmeasure q0[12] -> c0[12];\nmeasure q0[13] -> c0[13];\nmeasure q0[14] -> c0[14];\nmeasure q0[15] -> c0[15];\nmeasure q0[16] -> c0[16];\nmeasure q0[17] -> c0[17];\nmeasure q0[18] -> c0[18];\nmeasure q0[19] -> c0[19];\n", - "name": "circuit1", - "number_of_clbits": 20, - "number_of_qubits": 20, - "qubit_labels": [ - [ - "q0", - 0 - ], - [ - "q0", - 1 - ], - [ - "q0", - 2 - ], - [ - "q0", - 3 - ], - [ - "q0", - 4 - ], - [ - "q0", - 5 - ], - [ - "q0", - 6 - ], - [ - "q0", - 7 - ], - [ - "q0", - 8 - ], - [ - "q0", - 9 - ], - [ - "q0", - 10 - ], - [ - "q0", - 11 - ], - [ - "q0", - 12 - ], - [ - "q0", - 13 - ], - [ - "q0", - 14 - ], - [ - "q0", - 15 - ], - [ - "q0", - 16 - ], - [ - "q0", - 17 - ], - [ - "q0", - 18 - ], - [ - "q0", - 19 - ] - ] - }, - "instructions": [ - { - "name": 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"1.05922647838234", - "-2.00384727550396" - ] - }, - { - "name": "u3", - "params": [ - 2.5829343470681723, - 0.7549417940631911, - -3.803723285666508 - ], - "qubits": [ - 17 - ], - "texparams": [ - "2.58293434706817", - "0.754941794063191", - "-3.80372328566651" - ] - }, - { - "name": "u3", - "params": [ - 2.0993572859622427, - 4.457784570814218, - -1.6861919720084486 - ], - "qubits": [ - 16 - ], - "texparams": [ - "2.09935728596224", - "4.45778457081422", - "-1.68619197200845" - ] - }, - { - "name": "u3", - "params": [ - 1.459096217261041, - -1.8091266935363683, - 1.0350779754923756 - ], - "qubits": [ - 15 - ], - "texparams": [ - "1.45909621726104", - "-1.80912669353637", - "1.03507797549238" - ] - }, - { - "name": "u3", - "params": [ - 1.6089195995132182, - 0.8362950922017118, - -3.3207849863840346 - ], - "qubits": [ - 14 - ], - "texparams": [ - "1.60891959951322", - "0.836295092201712", - "-3.32078498638403" - ] - }, - { - "name": "u3", - "params": [ - 2.174003382373335, - 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"3.70673489056137", - "-0.863162140198313" - ] - }, - { - "name": "barrier", - "qubits": [ - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19 - ] - }, - { - "clbits": [ - 19 - ], - "memory": [ - 19 - ], - "name": "measure", - "qubits": [ - 19 - ] - }, - { - "clbits": [ - 18 - ], - "memory": [ - 18 - ], - "name": "measure", - "qubits": [ - 18 - ] - }, - { - "clbits": [ - 17 - ], - "memory": [ - 17 - ], - "name": "measure", - "qubits": [ - 17 - ] - }, - { - "clbits": [ - 16 - ], - "memory": [ - 16 - ], - "name": "measure", - "qubits": [ - 16 - ] - }, - { - "clbits": [ - 15 - ], - "memory": [ - 15 - ], - "name": "measure", - "qubits": [ - 15 - ] - }, - { - "clbits": [ - 14 - ], - "memory": [ - 14 - ], - "name": "measure", - "qubits": [ - 14 - ] - }, - { - "clbits": [ - 13 - ], - "memory": [ - 13 - ], - "name": "measure", - "qubits": [ - 13 - ] - }, - { - "clbits": [ - 12 - ], - "memory": [ - 12 - ], - "name": "measure", - "qubits": [ - 12 - ] - }, - { - "clbits": [ - 11 - ], - "memory": [ - 11 - ], - "name": "measure", - "qubits": [ - 11 - ] - }, - { - "clbits": [ - 10 - ], - "memory": [ - 10 - ], - "name": "measure", - "qubits": [ - 10 - ] - }, - { - "clbits": [ - 9 - ], - "memory": [ - 9 - ], - "name": "measure", - "qubits": [ - 9 - ] - }, - { - "clbits": [ - 8 - ], - "memory": [ - 8 - ], - "name": "measure", - "qubits": [ - 8 - ] - }, - { - "clbits": [ - 7 - ], - "memory": [ - 7 - ], - "name": "measure", - "qubits": [ - 7 - ] - }, - { - "clbits": [ - 6 - ], - "memory": [ - 6 - ], - "name": "measure", - "qubits": [ - 6 - ] - }, - { - "clbits": [ - 5 - ], - "memory": [ - 5 - ], - "name": "measure", - "qubits": [ - 5 - ] - }, - { - "clbits": [ - 4 - ], - "memory": [ - 4 - ], - "name": "measure", - "qubits": [ - 4 - ] - }, - { - "clbits": [ - 3 - ], - "memory": [ - 3 - ], - "name": "measure", - "qubits": [ - 3 - ] - }, - { - "clbits": [ - 2 - ], - "memory": [ - 2 - ], - "name": "measure", - "qubits": [ - 2 - ] - }, - { - "clbits": [ - 1 - ], - "memory": [ - 1 - ], - "name": "measure", - "qubits": [ - 1 - ] - }, - { - "clbits": [ - 0 - ], - "memory": [ - 0 - ], - "name": "measure", - "qubits": [ - 0 - ] - } - ] - } - ], - "header": { - "backend_name": "local_qasm_simulator_cpp" - }, - "qobj_id": "f58232aa-c07d-4512-ab0a-91f8ac73bafb", - "schema_version": "1.0.0", - "type": "QASM" -} \ No newline at end of file diff --git a/test/qobj/snapshot_pauli.json b/test/qobj/snapshot_pauli.json deleted file mode 100644 index 30b3d8efcb..0000000000 --- a/test/qobj/snapshot_pauli.json +++ /dev/null @@ -1,61 +0,0 @@ -{ - "qobj_id": "pauli_observable_snapshot_example", - "schema_version": "1.0.0", - "type": "QASM", - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 2, - "n_qubits": 2 - }, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "x", "qubits": [1]}, - { - "name": "snapshot", - "type": "expval_pauli", - "label": " pre_measure", - "qubits": [0, 1], - "params": [[1, "ZI"]] - }, - { - "name": "snapshot", - "type": "expval_pauli", - "label": " pre_measure", - "qubits": [0, 1], - "params": [[1, "IZ"]] - }, - { - "name": "snapshot", - "type": "expval_pauli", - "label": " pre_measure", - "qubits": [0, 1], - "params": [[1, "XI"]] - }, - { - "name": "snapshot", - "type": "expval_pauli", - "label": " pre_measure", - "qubits": [0, 1], - "params": [[1, "IX"]] - }, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]}, - { - "name": "snapshot", - "type": "expval_pauli", - "label": "post_measure", - "qubits": [0, 1], - "params": [[1, "ZZ"]] - }, - { - "name": "snapshot", - "type": "expval_pauli", - "label": "post_measure", - "qubits": [0, 1], - "params": [[1, "ZI"], [1, "IZ"]] - } - ] - } - ] -} \ No newline at end of file diff --git a/test/qobj/snapshot_probs.json b/test/qobj/snapshot_probs.json deleted file mode 100644 index 8a6acdca2e..0000000000 --- a/test/qobj/snapshot_probs.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "qobj_id": "probabilities_snapshot_example", - "schema_version": "1.0.0", - "type": "QASM", - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 2, - "n_qubits": 2 - }, - "instructions": [ - {"name": "h", "qubits": [0]}, - {"name": "cx", "qubits": [0, 1]}, - {"name": "snapshot", "type": "probabilities", - "label": "pre_measure", "params": [0, 1]}, - {"name": "measure", "qubits": [0, 1], "memory": [0, 1]}, - {"name": "snapshot", "type": "probabilities", - "label": "post_measure", "params": [0, 1]} - ] - } - ] -} \ No newline at end of file diff --git a/test/qobj/snapshot_state.json b/test/qobj/snapshot_state.json deleted file mode 100644 index 55f7df0ced..0000000000 --- a/test/qobj/snapshot_state.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "qobj_id": "state_snapshot_example", - "schema_version": "1.0.0", - "type": "QASM", - "experiments": [ - { - "config": { - "shots": 1000, - "memory_slots": 0, - "n_qubits": 2 - }, - "instructions": [ - {"name": "snapshot", "type": "state", "label": "initial"}, - {"name": "h", "qubits": [0]}, - {"name": "snapshot", "type": "state", "label": "middle"}, - {"name": "cx", "qubits": [0, 1]}, - {"name": "snapshot", "type": "state", "label": "final"} - ] - } - ] -} \ No newline at end of file diff --git a/test/src/test_snapshot.cpp b/test/src/test_snapshot.cpp index d09fe6d44c..ae63c8958b 100644 --- a/test/src/test_snapshot.cpp +++ b/test/src/test_snapshot.cpp @@ -12,13 +12,13 @@ namespace Test{ TEST_CASE( "Simulators Snapshot", "[snaphot]" ) { std::map qobj_snapshots; qobj_snapshots["state"] = - AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_state.json"); + AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_statevector.json"); qobj_snapshots["probs"] = AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_probs.json"); qobj_snapshots["pauli"] = - AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_pauli.json"); + AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_expval_pauli.json"); qobj_snapshots["matrix"] = - AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_matrix.json"); + AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_expval_matrix.json"); using State = AER::QubitVector::State<>; AER::Simulator::QasmController sim{}; diff --git a/test/src/test_snapshot_bdd.cpp b/test/src/test_snapshot_bdd.cpp index 84b53d686d..ddd8a47177 100644 --- a/test/src/test_snapshot_bdd.cpp +++ b/test/src/test_snapshot_bdd.cpp @@ -14,13 +14,13 @@ SCENARIO("We can get snapshots from different simulator types") { std::map qobj_snapshots; qobj_snapshots["state"] = - AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_state.json"); + AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_statevector.json"); qobj_snapshots["probs"] = AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_probs.json"); qobj_snapshots["pauli"] = - AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_pauli.json"); + AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_expval_pauli.json"); qobj_snapshots["matrix"] = - AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_matrix.json"); + AER::Test::Utilities::load_qobj("../../test/data/qobj_snapshot_expval_matrix.json"); using State = AER::QubitVector::State<>; AER::Simulator::QasmController sim{};