{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a5e2004",
   "metadata": {},
   "source": [
    "# How to perform optimization and simulation in the same calculation\n",
    "**Perform calculations using optimization results in a single graph**\n",
    "\n",
    "In Boulder Opal the highly-flexible optimization engine can easily be combined with simulation nodes to perform both optimization and simulation, all in a single graph. \n",
    "This means that one can first optimize the parameters of a Hamiltonian for some task, and then simulate the evolution with the optimized parameters. \n",
    "Note that adding simulation nodes to the optimization graph doesn't create any overhead to the calculation (as long as the cost node does not depend on the additional nodes), as the added nodes are only calculated after the optimization has been completed.\n",
    "If instead you want to reuse graphs or parts of a graph in different calculations, see the user guide [How to reuse graph definitions in different calculations](https://docs.q-ctrl.com/boulder-opal/toolkit/design/calculate-with-graphs/how-to-reuse-graph-definitions-in-different-calculations).\n",
    "\n",
    "Below we show an example of how to optimize a pulse, and then simulate evolution under the optimized pulse, in a single graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c13704-9f42-47a9-98c0-5ebfc0cfc56d",
   "metadata": {},
   "source": [
    "## Summary workflow\n",
    "### 1. Define the optimization computational graph\n",
    "Start by setting up a [graph object](https://docs.q-ctrl.com/boulder-opal/toolkit/design/calculate-with-graphs/how-to-represent-quantum-systems-using-graphs) to carry out your optimization, be it to perform a [control optimization](https://docs.q-ctrl.com/boulder-opal/toolkit/design/design-model-based-controls/how-to-optimize-controls-in-arbitrary-quantum-systems-using-graphs) or [system identification](https://docs.q-ctrl.com/boulder-opal/toolkit/design/characterize-hardware/how-to-perform-parameter-estimation-with-a-small-amount-of-data) task.\n",
    "\n",
    "### 2. Add nodes using the optimized results\n",
    "Add nodes to the same graph that use the optimized results, such as calculating [time-evolution operators](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/graph/Graph/time_evolution_operators_pwc) with the optimized controls, or the [Hessian](https://docs.q-ctrl.com/references/boulder-opal/boulderopal/graph/Graph/hessian) to estimate the confidence region of the estimated parameters.\n",
    "\n",
    "### 3. Run graph-based optimization\n",
    "With the graph object created, an optimization can be run using the `boulderopal.run_optimization` function. In addition to returning the results of the optimization, this function will also evaluate the nodes in `output_node_names` using the optimized variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d370d7-1fd2-4b1b-9720-601b3c54acec",
   "metadata": {},
   "source": [
    "## Example: Pulse optimization and simulation of a qutrit in a single graph\n",
    "In this example we apply a pulse to a qubit subject to leakage to an additional level. The resulting qutrit is treated as an oscillator (truncated to three levels) with an anharmonicity of $\\chi$, described by the Hamiltonian:\n",
    "\n",
    "$$\n",
    "H(t) = \\frac{\\chi}{2} (a^\\dagger)^2 a^2 + \\frac{\\Omega(t)}{2} a + \\frac{\\Omega^*(t)}{2} a^\\dagger,\n",
    "$$\n",
    "\n",
    "where $a = \\left|0 \\right\\rangle \\left\\langle 1 \\right| + \\sqrt{2} \\left|1 \\right\\rangle \\left\\langle 2 \\right|$ is the lowering operator and $\\Omega(t)$ is a time-dependent Rabi rate. The form of $\\Omega(t)$ is \n",
    "$$\\Omega(t) = A \\tanh\\left( \\frac{t - t_0}{T} \\right)$$\n",
    "where the amplitude $A$, and center time $t_0$, are optimizable parameters, and $T$ is the duration of the pulse.\n",
    "\n",
    "We choose as our target an X gate between the states $\\left| 0 \\right\\rangle$ and $\\left| 1 \\right\\rangle$. Notice that this target is not unitary in the total Hilbert space, but is still a valid target because it is a partial isometry—in other words, it is unitary in the subspace whose basis is $\\{ \\left| 0 \\right\\rangle, \\left| 1 \\right\\rangle \\}$.\n",
    "\n",
    "After optimizing $A$ and $t_0$ to produce an X gate, we evolve the qutrit state $|0\\rangle$ under the optimized Hamiltonian, expecting it to evolve to $|1\\rangle$. We optimize and simulate in a single graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b74d9509",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import qctrlvisualizer as qv\n",
    "import boulderopal as bo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "077a016f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1829423\") has started.\n",
      "Your task (action_id=\"1829423\") has completed.\n",
      "Final infidelity: 1.9e-06\n",
      "Total time evolution operator:\n",
      "[[-0.   +0.j    -0.011+1.j    -0.   +0.001j]\n",
      " [-0.01 +1.j     0.   -0.j     0.002-0.j   ]\n",
      " [ 0.   -0.002j -0.001+0.j     1.   -0.021j]]\n"
     ]
    }
   ],
   "source": [
    "n_levels = 3  # The number of levels in the system.\n",
    "\n",
    "# Define the Pulse parameters.\n",
    "segment_count = 50\n",
    "duration = 10e-6  # s\n",
    "\n",
    "graph = bo.Graph()\n",
    "\n",
    "# Define the anharmonic term.\n",
    "chi = 2 * np.pi * 3 * 1e8  # rad/s\n",
    "n = graph.number_operator(n_levels)\n",
    "anharmonic_drift = 0.5 * chi * (n @ n - n)\n",
    "\n",
    "# Define Rabi drive term.\n",
    "omega_end_value = graph.optimizable_scalar(lower_bound=0.0, upper_bound=3e7)\n",
    "omega_center_time = graph.optimizable_scalar(lower_bound=0.0, upper_bound=duration)\n",
    "omega_pulse = graph.signals.tanh_ramp_pwc(\n",
    "    duration=duration,\n",
    "    segment_count=segment_count,\n",
    "    end_value=omega_end_value,\n",
    "    center_time=omega_center_time,\n",
    "    ramp_duration=duration,\n",
    "    name=\"$\\\\Omega$\",\n",
    ")\n",
    "\n",
    "a = graph.annihilation_operator(n_levels)\n",
    "rabi_drive = graph.hermitian_part(omega_pulse * a)\n",
    "\n",
    "# Define Hamiltonian.\n",
    "hamiltonian = anharmonic_drift + rabi_drive\n",
    "\n",
    "# Define the target (X gate).\n",
    "target = np.zeros((n_levels, n_levels))\n",
    "target[0, 1] = 1\n",
    "target[1, 0] = 1\n",
    "target = graph.target(operator=target)\n",
    "\n",
    "# Calculate the infidelity.\n",
    "infidelity = graph.infidelity_pwc(\n",
    "    hamiltonian=hamiltonian, target=target, name=\"infidelity\"\n",
    ")\n",
    "\n",
    "# All the nodes we add below do not influence the cost node (infidelity).\n",
    "\n",
    "# Define the initial state.\n",
    "initial_state = graph.fock_state(n_levels, 0)[:, None]\n",
    "\n",
    "# Calculate the time evolution operators.\n",
    "sample_times = np.linspace(0, duration, 100)\n",
    "unitaries = graph.time_evolution_operators_pwc(\n",
    "    hamiltonian=hamiltonian, sample_times=sample_times, name=\"time_evolution_operators\"\n",
    ")\n",
    "\n",
    "evolved_states = unitaries @ initial_state\n",
    "evolved_states.name = \"evolved_states\"\n",
    "\n",
    "# Run optimization and simulation.\n",
    "result = bo.run_optimization(\n",
    "    graph=graph,\n",
    "    cost_node_name=\"infidelity\",\n",
    "    output_node_names=[\n",
    "        \"infidelity\",\n",
    "        \"$\\\\Omega$\",\n",
    "        \"time_evolution_operators\",\n",
    "        \"evolved_states\",\n",
    "    ],\n",
    "    optimization_count=4,\n",
    ")\n",
    "\n",
    "# Extract and print final infidelity.\n",
    "print(f\"Final infidelity: {result['output'].pop('infidelity')['value']:.1e}\")\n",
    "\n",
    "# Extract and print final time evolution operator.\n",
    "print(\"Total time evolution operator:\")\n",
    "print(np.round(result[\"output\"].pop(\"time_evolution_operators\")[\"value\"][-1], 3))\n",
    "\n",
    "# Extract and plot state populations.\n",
    "state_vectors = result[\"output\"].pop(\"evolved_states\")[\"value\"]\n",
    "final_populations = np.abs(state_vectors) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "943d292a-9c1b-4f9c-9788-8599da5525cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot populations evolution.\n",
    "qv.plot_population_dynamics(\n",
    "    sample_times,\n",
    "    {rf\"$|{state}\\rangle$\": final_populations[:, state] for state in range(n_levels)},\n",
    ")\n",
    "\n",
    "# Plot optimized control.\n",
    "qv.plot_controls(result[\"output\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
