1ising_model-2d--ibm.ipynb

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   "source": [
    "In this project the following paper served as guidance:"
   ]
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   "source": [
    "Evidence for the utility of quantum computing before fault tolerance. Nature, 615(7950), 596-600"
   ]
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    "This paper discusses the experimental evidence for the utility of quantum computing before achieving fault tolerance. The authors conducted experiments on a 127-qubit superconducting quantum processor, demonstrating that it can accurately measure expectation values for quantum circuits at a scale beyond what is feasible with brute-force classical computation. This suggests that quantum computing can offer advantages even in the current, noisy era, prior to the development of fully fault-tolerant quantum computers.\n",
    "\n",
    "The study focuses on running quantum circuits related to the time evolution of a 2D transverse-field Ising model. These circuits involve significant entanglement, making them challenging for classical methods to simulate accurately. The authors argue that this work provides a foundational tool for the realization of near-term quantum applications, showing that current quantum processors can be reliable enough to perform meaningful computations at scales unattainable by classical methods.\n",
    "\n",
    "**Reference:**\n",
    "Kim, Y., Eddins, A., Anand, S., Wei, K. X., van den Berg, E., Rosenblatt, S., Nayfeh, H., Wu, Y., Zaletel, M., Temme, K., & Kandala, A. (2023). Evidence for the utility of quantum computing before fault tolerance. *Nature*, *615*(7950), 596-600. https://doi.org/10.1038/s41586-023-06096-3"
   ]
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   "source": [
    "The base 2D-Ising model is derived from the 1D version displayed in the classiq-library on github:\n",
    "    \n",
    "    https://github.com/Classiq/classiq-library/blob/main/applications/physical_systems/ising_model/ising_model.ipynb \n",
    "authored by Amir Naveh."
   ]
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   "source": [
    "In this project a noiseless 2D Ising model is presented. It is then trotterized, noise is added to it and zero noise extrapolation applied. It is also run with depth and width constraints."
   ]
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   "source": [
    "# The Ising Model\n",
    "\n",
    "The Ising model is a pivotal concept in physics, originally developed to describe interactions between atomic spins in a lattice. While its initial focus was on magnetic dipole moments, the model's applications have expanded far beyond its origins. Today, it is instrumental in understanding phenomena in various fields, including neural networks, spin glasses, and the study of phase transitions.\n",
    "\n",
    "At its heart, the Ising model is defined by a Hamiltonian that captures the energy of a system based on the interactions between neighboring spins. The model is notable for its ability to represent complex systems with simple binary variables, making it a powerful tool in both theoretical and applied physics. Additionally, the Ising model serves as a foundation for solving intricate optimization problems. Its formulation as a Quadratic Unconstrained Binary Optimization (QUBO) problem allows it to be mapped onto various NP-hard challenges, such as the traveling salesman problem or protein folding.\n",
    "\n",
    "Using the Classiq platform, we demonstrate how the Ising model can be transformed into an optimization problem suitable for quantum computation. By employing Classiq's quantum approximate optimization algorithm (QAOA), we can approach the solution through quantum simulation. This method is versatile and can be adapted to run on different quantum simulators or even actual quantum hardware, showcasing the potential of quantum computing in addressing highly complex and computationally demanding problems."
   ]
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   "source": [
    "# 2-D Ising Model "
   ]
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   "source": [
    "## 0. Pre-requisites\n",
    "We need to ensure we have the required libraries:"
   ]
  },
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     "name": "stdout",
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     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q --upgrade pip "
   ]
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     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "qiskit-ibm-runtime 0.25.0 requires pydantic>=2.5.0, but you have pydantic 1.10.17 which is incompatible.\n",
      "qc-grader 0.19.7 requires networkx==3.2.1, but you have networkx 2.8.8 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q pyomo\n",
    "%pip install -q classiq \n",
    "%pip install -q matplotlib "
   ]
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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pyomo.core as pyo\n",
    "from classiq import * \n",
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "from classiq import set_execution_preferences, write_qmod, show, synthesize, execute\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "import pandas as pd\n",
    "from classiq.applications.combinatorial_optimization import get_optimization_solution_from_pyo\n"
   ]
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    "### 1. Defining the Optimization Problem\n",
    "\n",
    "\n",
    "In this step, we will build a 2D Ising model, which is a widely-used model for understanding the interactions of spins on a lattice. Each spin in this model can be either +1 or -1, representing two possible states.\n",
    "\n",
    "The Ising model describes how these spins interact with each other and with an external magnetic field. Specifically, you will define two key parameters:\n",
    "\n",
    "- **Interaction Coupling (J):** This parameter controls how strongly neighboring spins influence each other. If \\( J \\) is positive, the spins tend to align in the same direction (ferromagnetic interaction). If \\( J \\) is negative, the spins prefer to align in opposite directions (antiferromagnetic interaction).\n",
    "  \n",
    "- **Magnetic Field (h):** This parameter represents an external magnetic field that affects each spin individually, encouraging them to align with the field.\n",
    "\n",
    "In this model, the user has the option to apply **periodic boundary conditions**. This means that the edges of the lattice are connected, so the spins on the edge interact with the spins on the opposite edge. This setup simulates an infinite lattice by avoiding edge effects and ensuring that each spin has the same number of neighbors.\n",
    "\n",
    "By choosing the values for \\( J \\) and \\( h \\), and deciding whether to use periodic boundary conditions, you can explore how different physical conditions affect the behavior of the system. This is particularly useful for studying phenomena like phase transitions and the overall magnetization of the system.\n",
    "\n"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "def ising_model_2d(J: int, h: int, rows: int, cols: int, periodic: str) -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel(\"ising_2d\")\n",
    "\n",
    "    # Define the variables:\n",
    "    model.z = pyo.Var(range(rows * cols), domain=pyo.Binary)\n",
    "    z_array = np.array(list(model.z.values())).reshape((rows, cols))\n",
    "\n",
    "    def E(i, j, k, l):\n",
    "        return -J * (2 * z_array[i, j] - 1) * (2 * z_array[k, l] - 1) - (h / 2) * (\n",
    "            (2 * z_array[i, j] - 1) + (2 * z_array[k, l] - 1)\n",
    "        )\n",
    "\n",
    "    # Create the Ising Hamiltonian\n",
    "    model.H = 0\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            if j + 1 < cols:\n",
    "                model.H += E(i, j, i, j + 1)\n",
    "            if i + 1 < rows:\n",
    "                model.H += E(i, j, i + 1, j)\n",
    "            if periodic == \"True\":\n",
    "                if j == cols - 1:\n",
    "                    model.H += E(i, j, i, 0)\n",
    "                if i == rows - 1:\n",
    "                    model.H += E(i, j, 0, j)\n",
    "\n",
    "    # Setting the objective:\n",
    "    model.cost = pyo.Objective(expr=model.H, sense=pyo.minimize)\n",
    "\n",
    "    return model\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## 2. Create the 2D Ising model\n",
    "Instantiate the 2D Ising model with the chosen parameters: \n",
    "- coupling J\n",
    "- magnetic field h\n",
    "- boundary conditions periodic or not"
   ]
  },
  {
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   "source": [
    "ising_model = ising_model_2d(J=10, h=-20, rows=4, cols=4, periodic=\"True\")\n"
   ]
  },
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   "source": [
    "## 3. Optimize Using Quantum Optimization Algorithm\n",
    "Set up and run the optimization using QAOA:\n",
    "Next, we will develop a QAOA (Quantum Approximate Optimization Algorithm) model to solve the optimization problem. The output of this model will be the sequence of qubit values that represent the spin system's configuration with the lowest energy state."
   ]
  },
  {
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     "text": [
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n"
     ]
    }
   ],
   "source": [
    "qaoa_config = QAOAConfig(num_layers=5)\n",
    "optimizer_config = OptimizerConfig(max_iteration=100, alpha_cvar=0.7)\n",
    "\n",
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=ising_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)\n",
    "write_qmod(qmod, \"ising_model_2d\")\n",
    "\n",
    "constraints = Constraints(max_width = 100, optimization_parameter = \"depth\")\n",
    "qmod = set_constraints(qmod,constraints=constraints)\n",
    "qprog = synthesize(qmod)\n",
    "circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for depth\")\n",
    "print(f\"The circuit composition is:\")\n",
    "print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "constraints = Constraints(max_depth = 100, optimization_parameter = \"width\")\n",
    "qmod = set_constraints(qmod,constraints=constraints)\n",
    "qprog = synthesize(qmod)\n",
    "#show(qprog)\n",
    "circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for width\")\n",
    "print(f\"The circuit composition is:\")\n",
    "print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "res = execute(qprog).result()\n"
   ]
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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fdc6e80-c189-41ac-800c-edd861024487",
   "metadata": {},
   "source": [
    "\n",
    "After about 76 iterations a stable solution is  found. The number of qubits needed depends on the number of spins of the system. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6ebac8-69d2-4a16-8f5a-87a1750c3e10",
   "metadata": {},
   "source": [
    "## 4. Present Quantum Results\n",
    "Retrieve and present the optimization results: \n",
    "Since this is a quantum solution with probabilistic results, there is a defined probability for each result to be obtained by a measurement (presented by an histogram), where the solution is chosen to be the most probable one.\n",
    "\n",
    "We remind that in the notation of the solution \"0\" indicate \"-1\" spin value, and \"1\" indicates \"1\" spin value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15df8406-af67-48bf-822e-088ec4f472d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.146484</td>\n",
       "      <td>-960.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.013184</td>\n",
       "      <td>-800.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.012207</td>\n",
       "      <td>-800.0</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.008301</td>\n",
       "      <td>-800.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.007324</td>\n",
       "      <td>-800.0</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    probability   cost                                          solution  \\\n",
       "0      0.146484 -960.0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1      0.013184 -800.0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]   \n",
       "2      0.012207 -800.0  [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "13     0.008301 -800.0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]   \n",
       "14     0.007324 -800.0  [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "\n",
       "    count  \n",
       "0     300  \n",
       "1      27  \n",
       "2      25  \n",
       "13     17  \n",
       "14     15  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vqe_result = res[0].value\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    ising_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b9cdc739-985a-4d4c-978b-7ce975696bdd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c5cafa-a93b-4528-9cc9-9e04e94b6d3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fddf740a-f82c-44ba-b1c8-5f956aff3fc8",
   "metadata": {},
   "source": [
    "## 2D Ising Model Ground State Energy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df169f7b-caa2-40d0-b234-bde8b274d037",
   "metadata": {},
   "source": [
    "Calculating the exact ground state energy for a 2D Ising model using brute-force enumeration involves iterating over all possible spin configurations for a 2D grid. Given the exponential growth in the number of configurations, this approach is feasible only for small grids. Below is the Python code for this approach:\n",
    "\n",
    "### Brute-force Enumeration Method for 2D Ising Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52cee116-e68a-4956-a6f0-b31dff64d2f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground State Energy (2D): -64\n",
      "Ground State Configuration:\n",
      "[[-1 -1 -1 -1]\n",
      " [-1 -1 -1 -1]\n",
      " [-1 -1 -1 -1]\n",
      " [-1 -1 -1 -1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from itertools import product\n",
    "\n",
    "def calculate_ising_energy_2d(spins, J, h, L, periodic):\n",
    "    energy = 0\n",
    "    for i in range(L):\n",
    "        for j in range(L):\n",
    "            S = spins[i][j]\n",
    "            if i < L - 1:  # Interaction with right neighbor\n",
    "                energy -= J * S * spins[i + 1][j]\n",
    "            elif periodic:  # Periodic boundary interaction\n",
    "                energy -= J * S * spins[0][j]\n",
    "            if j < L - 1:  # Interaction with bottom neighbor\n",
    "                energy -= J * S * spins[i][j + 1]\n",
    "            elif periodic:  # Periodic boundary interaction\n",
    "                energy -= J * S * spins[i][0]\n",
    "            energy -= h * S  # Magnetic field contribution\n",
    "    return energy\n",
    "\n",
    "def ising_model_2d_exact(J, h, L, periodic=True):\n",
    "    # Generate all possible spin configurations for an LxL grid\n",
    "    all_spins = product([-1, 1], repeat=L*L)\n",
    "    \n",
    "    ground_state_energy = float('inf')\n",
    "    ground_state_configuration = None\n",
    "    \n",
    "    for spins in all_spins:\n",
    "        spins = np.array(spins).reshape((L, L))\n",
    "        energy = calculate_ising_energy_2d(spins, J, h, L, periodic)\n",
    "        if energy < ground_state_energy:\n",
    "            ground_state_energy = energy\n",
    "            ground_state_configuration = spins\n",
    "    \n",
    "    return ground_state_energy, ground_state_configuration\n",
    "\n",
    "# Example usage\n",
    "J = 1\n",
    "h = -2\n",
    "L = 4\n",
    "periodic = True\n",
    "\n",
    "ground_state_energy, ground_state_configuration = ising_model_2d_exact(J, h, L, periodic)\n",
    "print(\"Ground State Energy (2D):\", ground_state_energy)\n",
    "print(\"Ground State Configuration:\")\n",
    "print(ground_state_configuration)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3747f563-5d6c-47ff-bf9d-287ac44b3041",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Explanation:\n",
    "calculate_ising_energy_2d: This function calculates the energy of a given spin configuration for a 2D grid. It sums the interaction terms between neighboring spins and the contribution from the magnetic field. If the periodic boundary condition is specified, it also includes interactions across the boundaries.\n",
    "ising_model_2d_exact: This function generates all possible spin configurations for an \n",
    "𝐿\n",
    "×\n",
    "𝐿\n",
    "L×L grid. It then calculates the energy of each configuration using calculate_ising_energy_2d and keeps track of the configuration with the lowest energy (ground state).\n",
    "## Example Usage:\n",
    "We specify the interaction coupling \n",
    "𝐽\n",
    "J, the magnetic field \n",
    "ℎ\n",
    "h, the size of the grid \n",
    "𝐿\n",
    "L, and whether the boundary conditions are periodic.\n",
    "The function ising_model_2d_exact computes the ground state energy and the corresponding spin configuration by brute-force enumeration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e5a491-d0a0-4bdf-a697-ebc55dd63429",
   "metadata": {},
   "source": [
    "## Limitations:\n",
    "This brute-force approach is computationally expensive and feasible only for small grids (e.g., \n",
    "𝐿\n",
    "≤\n",
    "4\n",
    "L≤4). For larger grids, the number of configurations grows exponentially, making this method impractical.\n",
    "For larger systems, more advanced techniques such as Monte Carlo simulations or optimization algorithms should be used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e0ac1d-140c-448f-9f63-fac7348003d4",
   "metadata": {},
   "source": [
    "To show a convergence graph for the ground state energy of the 2D Ising model, we can use an iterative optimization algorithm instead of brute-force enumeration. One common approach is to use simulated annealing or a similar heuristic optimization method, which iteratively improves the solution.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37f4249d-78a6-40df-bfd2-80118ee92937",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ec98e18e-da68-4c54-aa45-573d2b33a89f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Trotterization \n",
    "\n",
    "Trotterization is a method in quantum computing used to approximate the evolution of complex quantum systems. It simplifies the process by breaking down the system's evolution into smaller, manageable steps. This allows us to simulate quantum behaviors on quantum computers, such as chemical reactions or material properties.\n",
    "\n",
    "By alternating between different interactions in small intervals, Trotterization helps approximate the system's overall behavior. Although it introduces some errors, these can be reduced by increasing the number of steps, making the simulation more accurate. In short, Trotterization is crucial for making complex quantum simulations feasible on quantum computers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59dd78b6-0b79-4101-8d9a-505404c1bcf0",
   "metadata": {},
   "source": [
    "To incorporate Trotterization into the 2D Ising model for quantum optimization,  need to follow these steps:\n",
    "\n",
    "## Update the Ising Model for Trotterization:\n",
    "\n",
    "Trotterization involves approximating the time evolution operator by decomposing it into simpler, smaller steps. For the Ising model, this usually means breaking down the Hamiltonian into parts and approximating the evolution operator with a series of these smaller steps.\n",
    "\n",
    "## Construct the Quantum Optimization Model:\n",
    "\n",
    "We use the Trotterized Hamiltonian in our quantum optimization model.\n",
    "Here’s how you can update your code to include Trotterization and prepare the model for QAOA optimization:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d4a860-e883-442e-9ad4-558cf058ce1e",
   "metadata": {},
   "source": [
    "# Steps:\n",
    "\n",
    "## 1. Hamiltonian Construction with Trotterization:\n",
    "\n",
    "The hamiltonian function calculates the Hamiltonian of the Ising model, taking into account Trotterization by dividing the Hamiltonian by the number of Trotter steps.\n",
    "\n",
    "## 2. Quantum Optimization Setup:\n",
    "\n",
    "The construct_combinatorial_optimization_model function prepares the model for quantum optimization using QAOA.\n",
    "\n",
    "## 3. Backend and Execution:\n",
    "\n",
    "The backend preferences and execution setup ensure that the model is run on the chosen quantum simulator.\n",
    "\n",
    "## 4. Convergence Graph:\n",
    "\n",
    "After running the optimization, the convergence graph helps visualize how the optimization process evolves.\n",
    "This code allows us to include Trotterization in the quantum optimization of the 2D Ising model, using QAOA and a quantum simulator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f7efb6-7f20-4343-aaea-931c75edce1c",
   "metadata": {},
   "source": [
    "To include Trotterization for the 2D Ising model, we need to decompose the Hamiltonian into smaller steps. For simplicity, we'll assume a straightforward Trotterization approach, where we split the Hamiltonian into two parts: the interaction terms and the magnetic field terms. Here's how we can modify our 2D Ising model to include Trotterization:\n",
    "\n",
    "## Updated 2D Ising Model with Trotterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5675b658-6a04-4b9d-ae69-530d6556bb39",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pyomo.environ as pyo\n",
    "import numpy as np\n",
    "from pyomo.core import ConcreteModel\n",
    "\n",
    "def ising_model_2d(J: int, h: int, rows: int, cols: int, periodic: str, trotter_steps: int) -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel(\"ising_2d\")\n",
    "\n",
    "    # Define the variables\n",
    "    model.z = pyo.Var(range(rows * cols), domain=pyo.Binary)\n",
    "    z_array = np.array(list(model.z.values())).reshape((rows, cols))\n",
    "\n",
    "    # Helper function to get spin value\n",
    "    def get_spin(i, j):\n",
    "        return 2 * z_array[i, j] - 1\n",
    "\n",
    "    # Interaction term\n",
    "    def interaction_term(i, j, k, l):\n",
    "        return -J * get_spin(i, j) * get_spin(k, l)\n",
    "\n",
    "    # Magnetic field term\n",
    "    def magnetic_field_term(i, j):\n",
    "        return -h * get_spin(i, j)\n",
    "\n",
    "    # Define the Hamiltonian with Trotterization\n",
    "    def hamiltonian(periodic, trotter_steps):\n",
    "        H_interaction = 0\n",
    "        H_magnetic = 0\n",
    "        \n",
    "        # Interaction terms\n",
    "        for i in range(rows):\n",
    "            for j in range(cols):\n",
    "                if j + 1 < cols:\n",
    "                    H_interaction += interaction_term(i, j, i, j + 1)\n",
    "                if i + 1 < rows:\n",
    "                    H_interaction += interaction_term(i, j, i + 1, j)\n",
    "                if periodic == \"True\":\n",
    "                    if j == cols - 1:\n",
    "                        H_interaction += interaction_term(i, j, i, 0)\n",
    "                    if i == rows - 1:\n",
    "                        H_interaction += interaction_term(i, j, 0, j)\n",
    "        \n",
    "        # Magnetic field terms\n",
    "        for i in range(rows):\n",
    "            for j in range(cols):\n",
    "                H_magnetic += magnetic_field_term(i, j)\n",
    "        \n",
    "        # Trotterization\n",
    "        H_total = (H_interaction + H_magnetic) / trotter_steps\n",
    "        return H_total\n",
    "\n",
    "    # Create the Hamiltonian with Trotterization\n",
    "    model.H = hamiltonian(periodic, trotter_steps)\n",
    "\n",
    "    # Set the objective\n",
    "    model.cost = pyo.Objective(expr=model.H, sense=pyo.minimize)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0dee7797-f67c-456f-a1cf-389aa083da2a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "J = 10\n",
    "h = -20\n",
    "rows = 4\n",
    "cols = 4\n",
    "periodic = \"True\"\n",
    "trotter_steps = 10  # Number of Trotter steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd48500-4220-43f5-b53c-4b7ae211705e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. Create Your 2D Ising Model\n",
    "Instantiate the 2D Ising model with the chosen parameters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d35e5466-e604-41ab-bf83-862f19b4db9b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create Ising model\n",
    "ising_model = ising_model_2d(J, h, rows, cols, periodic, trotter_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022b563c-6cb6-461c-b4cf-55b9c40b94bf",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "### 3. Optimize Using Quantum Optimization Algorithm\n",
    "Set up and run the optimization using QAOA:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a964192a-589b-48d9-88c8-6f61645df2c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The circuit width is 16 and the circuit_depth is 91 when optimized for depth\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n",
      "The circuit width is 16 and the circuit_depth is 91 when optimized for width\n",
      "The circuit composition is:\n",
      "{'cx': 320, 'rz': 240, 'rx': 80, 'h': 16}\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantum optimization with QAOA\n",
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=5)\n",
    "optimizer_config = OptimizerConfig(max_iteration=100, alpha_cvar=0.7)\n",
    "\n",
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=ising_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")\n",
    "\n",
    "# Set quantum backend\n",
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)\n",
    "\n",
    "# Write the QAOA model\n",
    "from classiq import write_qmod\n",
    "write_qmod(qmod, \"ising_model_2d_trotter\")\n",
    "\n",
    "# Create and show the quantum circuit\n",
    "from classiq import show, synthesize\n",
    "\n",
    "constraints = Constraints(max_width = 100, optimization_parameter = \"depth\")\n",
    "qmod = set_constraints(qmod,constraints=constraints)\n",
    "qprog = synthesize(qmod)\n",
    "circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for depth\")\n",
    "print(f\"The circuit composition is:\")\n",
    "print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "constraints = Constraints(max_depth = 100, optimization_parameter = \"width\")\n",
    "qmod = set_constraints(qmod,constraints=constraints)\n",
    "qprog = synthesize(qmod)\n",
    "#show(qprog)\n",
    "circuit_width = QuantumProgram.from_qprog(qprog).data.width\n",
    "circuit_depth = QuantumProgram.from_qprog(qprog).transpiled_circuit.depth\n",
    "print(f\"The circuit width is {circuit_width} and the circuit_depth is {circuit_depth} when optimized for width\")\n",
    "\n",
    "print(f\"The circuit composition is:\")\n",
    "print(QuantumProgram.from_qprog(qprog).transpiled_circuit.count_ops)\n",
    "\n",
    "# Execute the quantum program\n",
    "from classiq import execute\n",
    "res = execute(qprog).result()\n",
    "\n",
    "# Check the convergence graph\n",
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238b20a0-c811-4380-9cfa-c16346fcea00",
   "metadata": {},
   "source": [
    "\n",
    "Convrgence happens with the trotterized system after about 60 iterations, which is quicker than with the non-trotterized system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8cf5f5c-001e-45b9-9c25-24f1429e6144",
   "metadata": {},
   "outputs": [],
   "source": [
    "ibm_provider = IBMBackendProvider(\n",
    "    hub=\"Hub name\", group=\"Group name\", Project=\"Project name\"\n",
    ")\n",
    "ibm_preferences = IBMBackendPreferences(\n",
    "    backend_name=\"Name of requsted quantum hardware\",\n",
    "    access_token=\"A Valid API access token to IBM Quantum\",\n",
    "    provider=ibm_provider,\n",
    ")\n",
    "execution_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ibm_preferences\n",
    ")\n",
    "\n",
    "# Set the preferences\n",
    "model = set_execution_preferences(model, execution_preferences)\n",
    "\n",
    "# Create the model\n",
    "model = create_model(main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d9b5df6-b044-42a8-856e-8027af1b0c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "constraints = Constraints(\n",
    "    max_width=20, optimization_parameter=OptimizationParameter.DEPTH\n",
    ")\n",
    "model = set_constraints(model, constraints)\n",
    "\n",
    "\n",
    "quantum_model_with_preferences = set_preferences(\n",
    "    quantum_model,\n",
    "    Preferences(backend_service_provider=\"IBM Quantum\", backend_name=\"ibm_brisbane\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e9dc1c4-5610-414a-896d-7f51e0d82f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_model(main)\n",
    "constraints = Constraints(\n",
    "    optimization_parameter=OptimizationParameter.DEPTH, max_width=21\n",
    ")\n",
    "preferences = Preferences(\n",
    "    backend_service_provider=\"IBM Quantum\", backend_name=\"ibmq_kolkata\"\n",
    ")\n",
    "model = set_constraints(model, constraints)\n",
    "model = set_preferences(model, preferences)\n",
    "\n",
    "write_qmod(model, \"mcx_14_ctrl_hardware\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d01ceb2-daf4-4306-aa01-ad2620ae7de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq.execution import IBMBackendPreferences, IBMBackendProvider\n",
    "\n",
    "ibm_provider = IBMBackendProvider()\n",
    "\n",
    "preferences = IBMBackendPreferences(\n",
    "    backend_name=\"ibmq_lima\",\n",
    "    access_token=\"insert_token_number\",\n",
    "    provider=ibm_provider,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8a09001-983e-4fa9-9448-b0ac585cfcef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq.execution import IBMBackendPreferences, IBMBackendProvider\n",
    "\n",
    "ibm_provider = IBMBackendProvider()\n",
    "\n",
    "preferences = IBMBackendPreferences(\n",
    "    backend_name=\"ibmq_quito\",\n",
    "    access_token=\"insert_token_number\",\n",
    "    provider=ibm_provider,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86de4900-5adf-4b49-97e7-dc98612ea2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq.execution import IBMBackendPreferences, IBMBackendProvider\n",
    "\n",
    "ibm_provider = IBMBackendProvider()\n",
    "\n",
    "preferences = IBMBackendPreferences(\n",
    "    backend_name=\"ibmq_manila\",\n",
    "    access_token=\"insert_token_number\",\n",
    "    provider=ibm_provider,\n",
    ")\n"
   ]
  }
 ],
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