{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "intro",
   "metadata": {},
   "source": [
    "# Quick start\n",
    "\n",
    "This guide demonstrates a minimal working example of the `qiskit-addon-aqc-tensor` package. We use AQC-Tensor to compress a deep Trotter circuit into a shallower parametrized ansatz that approximates the same time-evolved state. To see examples of how to build realistic workflows with this tool and execute on quantum hardware, check out the tutorials on the IBM Quantum Platform ([Tutorial 1](https://quantum.cloud.ibm.com/docs/en/tutorials/reducing-circuit-depth-with-aqc))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inputs-header",
   "metadata": {},
   "source": [
    "## Prepare inputs for AQC-Tensor\n",
    "\n",
    "AQC-Tensor takes as input a deep target circuit (simulated as a tensor network) and a shallower circuit from which to derive the ansatz structure.\n",
    "\n",
    "We define a 10-site Ising Hamiltonian and generate both circuits from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "setup-inputs",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.synthesis import SuzukiTrotter\n",
    "from qiskit.transpiler import CouplingMap\n",
    "from qiskit_addon_utils.problem_generators import (\n",
    "    generate_time_evolution_circuit,\n",
    "    generate_xyz_hamiltonian,\n",
    ")\n",
    "\n",
    "coupling_map = CouplingMap.from_heavy_hex(3, bidirectional=False)\n",
    "reduced_coupling_map = coupling_map.reduce([0, 13, 1, 14, 10, 16, 4, 15, 3, 9])\n",
    "\n",
    "hamiltonian = generate_xyz_hamiltonian(\n",
    "    reduced_coupling_map,\n",
    "    coupling_constants=(0.0, 0.0, 1.0),\n",
    "    ext_magnetic_field=(0.4, 0.0, 0.0),\n",
    ")\n",
    "\n",
    "evolution_time = 4.0\n",
    "\n",
    "# Deep target circuit (many Trotter steps)\n",
    "target_circuit = generate_time_evolution_circuit(\n",
    "    hamiltonian,\n",
    "    synthesis=SuzukiTrotter(reps=45),\n",
    "    time=evolution_time,\n",
    ")\n",
    "\n",
    "# Shallow circuit (fewer Trotter steps) to define ansatz structure\n",
    "good_circuit = generate_time_evolution_circuit(\n",
    "    hamiltonian,\n",
    "    synthesis=SuzukiTrotter(reps=5),\n",
    "    time=evolution_time,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aqc-header",
   "metadata": {},
   "source": [
    "## Compress with AQC-Tensor\n",
    "\n",
    "We simulate the target circuit as a matrix-product state (MPS), generate a parametrized ansatz from the shallow circuit, and optimize its parameters to maximize fidelity with the target state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "run-aqc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import quimb.tensor\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "from qiskit_addon_aqc_tensor import generate_ansatz_from_circuit\n",
    "from qiskit_addon_aqc_tensor.objective import MaximizeStateFidelity\n",
    "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n",
    "from qiskit_addon_aqc_tensor.simulation.quimb import QuimbSimulator\n",
    "\n",
    "# Configure the MPS simulator\n",
    "simulator_settings = QuimbSimulator(\n",
    "    partial(quimb.tensor.CircuitMPS, max_bond=100, cutoff=1e-8),\n",
    "    autodiff_backend=\"jax\",\n",
    ")\n",
    "\n",
    "# Simulate target circuit as MPS\n",
    "target_mps = tensornetwork_from_circuit(target_circuit, simulator_settings)\n",
    "\n",
    "# Generate ansatz and initial parameters from the shallow circuit\n",
    "ansatz, initial_parameters = generate_ansatz_from_circuit(good_circuit, qubits_initially_zero=True)\n",
    "print(f\"Target circuit depth: {target_circuit.depth()}\")\n",
    "print(f\"Ansatz circuit depth: {ansatz.depth()}, with {len(initial_parameters)} parameters\")\n",
    "\n",
    "# Optimize ansatz parameters against the target MPS\n",
    "objective = MaximizeStateFidelity(target_mps, ansatz, simulator_settings)\n",
    "\n",
    "result = minimize(\n",
    "    objective.loss_function,\n",
    "    initial_parameters,\n",
    "    method=\"L-BFGS-B\",\n",
    "    jac=True,\n",
    "    options={\"maxiter\": 100},\n",
    ")\n",
    "\n",
    "print(f\"\\nFinal fidelity: {1 - result.fun:.8f}\")\n",
    "print(f\"Compressed circuit depth: {ansatz.assign_parameters(result.x).depth()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expected-output",
   "metadata": {},
   "source": [
    "```\n",
    "Target circuit depth: 270\n",
    "Ansatz circuit depth: 23, with 515 parameters\n",
    "\n",
    "Final fidelity: 0.99971010\n",
    "Compressed circuit depth: 23\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "next-steps",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "Check out the tutorials on the IBM Quantum Platform to see how to build realistic workflows with AQC-Tensor ([Tutorial 1](https://quantum.cloud.ibm.com/docs/en/tutorials/reducing-circuit-depth-with-aqc))."
   ]
  }
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