This is the code accompanying Asymptotic Improvements to Quantum Circuits via Qutrits.

The code is built on top of the 0.4.0.dev35 version of cirq. It can be installed by running "python setup.py install". The results of our simulations are in the results/ directory. Our results can be reproduced by following the code block below, for each of the different noise models.

The quantum trajectories style simulation is accomplished in the apply_unitary_effect_to_state(...) method. In principle, the code can be generalized for arbitrary qudit levels by setting QUDIT_LEVELS in cirq/__init__.py. However, the specific gate set and noise models provided here are for qutrits.

import cirq
from cirq import qutrit
import numpy as np

N = 14
g = qutrit.BTBCnUGate()
op = g(*cirq.LineQubit.range(N))
c = cirq.Circuit.from_ops(op.default_decompose(), strategy=cirq.InsertStrategy.EARLIEST)

# currently available noise models in cirq/circuits/circuit.py are:
# CurrentSuperconductingQCErrors, FutureSuperconductingQCErrors, FutureSuperconductingQCErrorsBetterT1,
# FutureSuperconductingQCErrorsBetterGates, FutureSuperconductingQCErrorsBetterT1AndGates,
# DressedQutritErrors, BareQutritErrors
noise_model = cirq.circuits.circuit.CurrentSuperconductingQCErrors()


def get_random_state(n):
    rand_state2 = np.zeros(2 ** n, np.complex64)
    rand_state2.real = np.random.randn(2 ** n)
    rand_state2.imag = np.random.randn(2 ** n)
    rand_state2 /= np.linalg.norm(rand_state2)
    rand_state3 = np.zeros(3 ** n, np.complex64)
    for i, val in enumerate(rand_state2):
        rand_state3[int(bin(i)[2:], 3)] = val
    return rand_state3

for _ in range(1000):
    print(c.apply_unitary_effect_to_state(get_random_state(N), noise_model=noise_model)[0])

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Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators.

Installation

Follow these instructions.

Hello Qubit

A simple example to get you up and running:

import cirq

# Pick a qubit.
qubit = cirq.GridQubit(0, 0)

# Create a circuit
circuit = cirq.Circuit.from_ops(
    cirq.X(qubit)**0.5,  # Square root of NOT.
    cirq.measure(qubit, key='m')  # Measurement.
)
print("Circuit:")
print(circuit)

# Simulate the circuit several times.
simulator = cirq.google.XmonSimulator()
result = simulator.run(circuit, repetitions=20)
print("Results:")
print(result)

Example output:

Circuit:
(0, 0): ───X^0.5───M('m')───
Results:
m=11000111111011001000

Documentation

See here or jump into the tutorial.

Contributing

We welcome contributions. Please follow these guidelines.

See Also

For those interested in using quantum computers to solve problems in chemistry and materials science, we encourage exploring OpenFermion and its sister library for compiling quantum simulation algorithms in Cirq, OpenFermion-Cirq.

Disclaimer

Copyright 2018 The Cirq Developers. This is not an official Google product.