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   "source": [
    "# How to simulate large open system dynamics\n",
    "**Calculate the dynamics of a high-dimensional quantum system described by a GKS–Lindblad master equation**\n",
    "\n",
    "Boulder Opal enables you to simulate the evolution of [open quantum systems](https://docs.q-ctrl.com/boulder-opal/toolkit/design/simulate-quantum-systems/how-to-simulate-open-system-dynamics) that are interacting with their environment. \n",
    "\n",
    "For larger systems, with Hilbert space dimensions more than roughly 10, computing the exact evolution can be prohibitively expensive. Instead, you can get better performance by using an approximate method to calculate the evolution.  You can express the dynamics of these open quantum systems in terms of a master equation, such as the one described by [Gorini, Kossakowski, and Sudarshan (GKS)](https://doi.org/10.1063/1.522979) and [Lindblad](https://doi.org/10.1007/BF01608499). The tools described in this notebook allow you to solve this GKS–Lindblad master equation to obtain the time evolution for large systems using approximate methods.\n",
    "\n",
    "## Summary workflow\n",
    "\n",
    "### 1. Calculate the evolution of a density matrix\n",
    "\n",
    "Boulder Opal provides the `graph.density_matrix_evolution_pwc` graph operation to obtain the solution of the GKS–Lindblad master equation at some sample times for a given piecewise-constant Hamiltonian and Lindblad operators.\n",
    "By passing the `error_tolerance` parameter to this function, you can easily switch between the exact and approximate methods.\n",
    "A value of None will compute the exact evolution, which can be grossly inefficient for large systems.\n",
    "Passing a numerical value for the tolerance will control the precision of the method; a smaller value leads to a more accurate solution, but might take longer computation time.\n",
    "However, setting it to a too small value (for example below 1e-12) would not further improve the precision, since the dominating error in that case is due to floating point error.\n",
    "A recommended value is around the default of 1e-6.\n",
    "Note that increasing the number or density of requested sample times will not affect the precision of the solution.\n",
    "\n",
    "\n",
    "### 2. Use sparse matrices (recommended)\n",
    "When you use the `error_tolerance` parameter, you have the option of setting up your system with sparse matrices.\n",
    "Using sparse matrices can lead to performance improvements for large systems if your Hamiltonian and collapse operators contain mostly zeros.\n",
    "\n",
    "\n",
    "## Example: Superconducting resonator coupled to transmon qubits\n",
    "In this example, we consider a superconducting qubit coupled to a transmission line resonator (truncated to $10$ levels), with a drive on the resonator. We show how to simulate a measurement process that uses the quadratures of the resonator output to probe the qubit state.\n",
    "\n",
    "Assuming that the qubit is far detuned from the resonator (the dispersive approximation), and that the resonator drive is off resonance from the qubit transition frequency, in the absence of losses the system can be described by the Hamiltonian (see [Gambetta et al.](https://arxiv.org/pdf/0709.4264.pdf) and [Blais et al.](https://arxiv.org/pdf/cond-mat/0402216.pdf)):\n",
    "\n",
    "$$ H(t) = \\frac{\\omega_q+g^2/\\Delta}{2} \\sigma_z + (\\omega_r-\\omega_d) a^\\dagger a + \\frac{g^2}{\\Delta} a^\\dagger a \\sigma_z + \\Gamma(t) a^\\dagger + \\Gamma^*(t) a, $$\n",
    "\n",
    "where $a$ is the annihilation operator for the resonator, $\\sigma_z$ is the Pauli Z matrix for the qubit, $\\Gamma(t)$ is the resonator drive, $\\omega_r$ is the resonator frequency, $\\omega_q$ is the qubit transition frequency, $\\omega_d$ is the resonator drive frequency, $\\Delta=\\omega_q-\\omega_r$ is the qubit detuning from the resonator, and $g$ is the coupling strength.\n",
    "\n",
    "The losses in the system can be described by two decoherent terms: the resonator photon loss $\\kappa$ (with associated Lindlbad operator $a$) and the qubit decay $\\gamma$ (with associated Lindlbad operator $\\sigma_-$), leading to the GKS–Lindblad equation describing the system evolution:\n",
    "\n",
    "$$ \\frac{\\mathrm{d} \\rho(t)}{\\mathrm{d} t} = - i \\left[ H(t), \\rho(t) \\right] + \\kappa \\left[ a \\rho(t) a^\\dagger - \\frac{1}{2} \\rho(t) a^\\dagger a - \\frac{1}{2} a^\\dagger a  \\rho(t) \\right] +\n",
    "\\gamma \\left[ \\sigma_- \\rho(t) \\sigma_+ - \\frac{1}{2} \\rho(t) \\sigma_+ \\sigma_- - \\frac{1}{2} \\sigma_+\\sigma_-  \\rho(t) \\right], $$\n",
    "\n",
    "where $\\sigma_{\\pm} = (\\sigma_x \\mp i\\sigma_y)/2$.\n",
    "\n",
    "Below we show how you can simulate this system using Boulder Opal. Note that similar code can be used to perform optimizations, for example if you want to design custom measurement pulses optimized for your particular system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.sparse import coo_matrix\n",
    "import qctrlvisualizer as qv\n",
    "import boulderopal as bo\n",
    "\n",
    "plt.style.use(qv.get_qctrl_style())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1829140\") has started.\n",
      "Your task (action_id=\"1829140\") has completed.\n"
     ]
    }
   ],
   "source": [
    "# Define qubit operators.\n",
    "identity_qubit = np.identity(2)\n",
    "sigma_z = np.array([[1.0, 0.0], [0.0, -1.0]])\n",
    "sigma_m = np.array([[0.0, 1.0], [0.0, 0.0]])\n",
    "\n",
    "# Define resonator operators.\n",
    "resonator_level_count = 10  # Number of levels\n",
    "identity_resonator = np.identity(resonator_level_count)\n",
    "a = np.diag(np.sqrt(np.arange(1, resonator_level_count)), k=1)\n",
    "a_T = a.T.conj()\n",
    "\n",
    "# Define system parameters.\n",
    "omega_r = 2 * np.pi * 10e9  # Hz\n",
    "omega_q = 2 * np.pi * 8e9  # Hz\n",
    "omega_d = 2 * np.pi * 10e9  # Hz\n",
    "delta = omega_q - omega_r\n",
    "g = 2 * np.pi * 0.05e9  # Hz\n",
    "kappa = 2 * np.pi * 1e6  # Hz\n",
    "gamma = 2 * np.pi * 1e3  # Hz\n",
    "duration = 100e-9  # s\n",
    "sample_times = np.linspace(0, duration, 500)\n",
    "\n",
    "# Define drive parameters.\n",
    "drive_amplitude = 2 * np.pi * 0.02e9  # Hz\n",
    "drive_rise_duration = 20e-9  # s\n",
    "segment_count = 1000\n",
    "\n",
    "# Set up Hamiltonian. We use the `coo_matrix` function from SciPy to\n",
    "# convert the matrix to a sparse matrix, which can lead to faster\n",
    "# computations for large systems.\n",
    "base_hamiltonian_value = coo_matrix(\n",
    "    (omega_q + g**2 / delta) / 2 * np.kron(identity_resonator, sigma_z)\n",
    "    + (omega_r - omega_d) * np.kron(a_T @ a, identity_qubit)\n",
    "    + g**2 / delta * np.kron(a_T @ a, sigma_z)\n",
    ")\n",
    "drive_hamiltonian_value = coo_matrix(drive_amplitude * np.kron(a_T + a, identity_qubit))\n",
    "\n",
    "# Set up Lindblad terms.\n",
    "lindblad_terms = [\n",
    "    (kappa, coo_matrix(np.kron(a, identity_qubit))),\n",
    "    (gamma, coo_matrix(np.kron(identity_resonator, sigma_m))),\n",
    "]\n",
    "\n",
    "# Set up density matrices for the qubit in |0> and |1> states.\n",
    "resonator_ground_state = np.eye(resonator_level_count)[0]\n",
    "psi_0 = np.kron(resonator_ground_state, [1, 0])\n",
    "rho_0 = np.outer(psi_0, psi_0)\n",
    "psi_1 = np.kron(resonator_ground_state, [0, 1])\n",
    "rho_1 = np.outer(psi_1, psi_1)\n",
    "\n",
    "# Set up observables for the quadratures of the resonator field.\n",
    "I = a_T + a\n",
    "Q = -1j * (a - a_T)\n",
    "\n",
    "# Define graph.\n",
    "graph = bo.Graph()\n",
    "\n",
    "# Set up constant part of the Hamiltonian.\n",
    "base_hamiltonian = graph.constant_sparse_pwc_operator(\n",
    "    duration=duration, operator=base_hamiltonian_value\n",
    ")\n",
    "\n",
    "# Set up drive term of the Hamiltonian with a tanh envelope.\n",
    "drive_envelope = graph.signals.tanh_ramp_pwc(\n",
    "    duration, segment_count, 1.0, drive_rise_duration, center_time=0.0\n",
    ")\n",
    "drive_hamiltonian = graph.sparse_pwc_operator(\n",
    "    signal=drive_envelope, operator=drive_hamiltonian_value\n",
    ")\n",
    "\n",
    "# Perform the open quantum system evolution.\n",
    "density_matrices = graph.density_matrix_evolution_pwc(\n",
    "    initial_density_matrix=np.array([rho_0, rho_1]),\n",
    "    hamiltonian=graph.sparse_pwc_sum([base_hamiltonian, drive_hamiltonian]),\n",
    "    lindblad_terms=lindblad_terms,\n",
    "    sample_times=sample_times,\n",
    "    error_tolerance=1e-7,\n",
    ")\n",
    "\n",
    "# Calculate reduced density matrices for the resonator.\n",
    "density_matrices_resonator = graph.partial_trace(\n",
    "    density_matrices, [resonator_level_count, 2], 1\n",
    ")\n",
    "\n",
    "# Compute expected value of I/Q quadratures.\n",
    "I_values = graph.trace(density_matrices_resonator @ I, name=\"I\")\n",
    "Q_values = graph.trace(density_matrices_resonator @ Q, name=\"Q\")\n",
    "\n",
    "# Run the simulation.\n",
    "result = bo.execute_graph(graph=graph, output_node_names=[\"I\", \"Q\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "times = sample_times * 1e9\n",
    "I_quadratures = np.real_if_close(result[\"output\"][\"I\"][\"value\"])\n",
    "Q_quadratures = np.real_if_close(result[\"output\"][\"Q\"][\"value\"])\n",
    "\n",
    "axs[0].set_title(\"I quadrature\")\n",
    "axs[0].plot(times, I_quadratures[0], label=r\"$|0\\rangle$\")\n",
    "axs[0].plot(times, I_quadratures[1], label=r\"$|1\\rangle$\")\n",
    "axs[0].set_xlabel(\"Time (ns)\")\n",
    "axs[0].set_ylabel(\"I (arb. units)\")\n",
    "\n",
    "axs[1].set_title(\"Q quadrature\")\n",
    "axs[1].plot(times, Q_quadratures[0], label=r\"$|0\\rangle$\")\n",
    "axs[1].plot(times, Q_quadratures[1], label=r\"$|1\\rangle$\")\n",
    "axs[1].set_xlabel(\"Time (ns)\")\n",
    "axs[1].set_ylabel(\"Q (arb. units)\")\n",
    "\n",
    "\n",
    "hs, ls = axs[0].get_legend_handles_labels()\n",
    "fig.legend(handles=hs, labels=ls, loc=\"center\", bbox_to_anchor=(0.5, 1.0), ncol=2)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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