{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af569e93",
   "metadata": {},
   "source": [
    "# Simulate one dimensional Fermi-Hubbard model dynamics using Fire Opal\n",
    "**Learn how to use Fire Opal to simulate the time evolution of a strongly correlated quantum system**\n",
    "\n",
    "Quantum dynamics simulation allows us to model how quantum states change over time under a given Hamiltonian. This tutorial demonstrates how to use Fire Opal to simulate a one-dimensional Fermi-Hubbard model on a real quantum processing unit (QPU).\n",
    "\n",
    "The Fermi-Hubbard model is a cornerstone of condensed-matter physics, used to describe electrons moving on a lattice with on-site interactions. Fire Opal manages the complex process of translating the fermionic Hamiltonian to a Pauli one (Jordan-Wigner transformation), Trotterization, layout selection, circuit compilation, and physical-layer error suppression to deliver highly accurate simulation results.\n",
    "\n",
    "## Tutorial contents\n",
    "\n",
    "- An overview of the 1D Fermi-Hubbard model and its Trotterized time evolution\n",
    "- Preparation of an arbitrary Fock state and site-occupation observables\n",
    "- Execution of the managed simulation workflow with `FermiHubbardRunner`\n",
    "- An optional step-by-step workflow for granular control\n",
    "- Asynchronous retrieval and post-processing of submitted hardware jobs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523e618c",
   "metadata": {},
   "source": [
    "## Requirements\n",
    "\n",
    "Install the required packages in the active notebook environment. Restart the kernel after installation when necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6a169fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install fireopal scipy matplotlib numpy qctrl-visualizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84bca93",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "The Fermi-Hubbard model describes spin-$\\tfrac{1}{2}$ fermions hopping on a lattice with an on-site interaction. On a one-dimensional chain of $L$ sites with open boundary conditions, the Hamiltonian in its most general form allows all couplings to vary across the lattice:\n",
    "\n",
    "$$\n",
    "H =\n",
    "-\\sum_{i=0}^{L-2}\n",
    "\\sum_{\\sigma \\in \\{\\uparrow,\\downarrow\\}}\n",
    "t_{h,i}\n",
    "\\left(\n",
    "c^\\dagger_{i\\sigma}c_{i+1,\\sigma}\n",
    "+ \\mathrm{h.c.}\n",
    "\\right)\n",
    "+\n",
    "\\sum_{i=0}^{L-1} U_i\\, n_{i\\uparrow}n_{i\\downarrow}\n",
    "-\n",
    "\\sum_{i=0}^{L-1}\n",
    "\\sum_\\sigma \\mu_i\\, n_{i\\sigma}.\n",
    "$$\n",
    "\n",
    "where, $c^\\dagger_{i\\sigma}$ and $c_{i\\sigma}$ create and annihilate a fermion with spin $\\sigma \\in \\{\\uparrow,\\downarrow\\}$ on site $i$, and $n_{i\\sigma}=c^\\dagger_{i\\sigma}c_{i\\sigma}$ is the corresponding number operator.\n",
    "\n",
    "The first term is the kinetic or hopping energy, with amplitude $t_{h,i}$ on the bond connecting sites $i$ and $i+1$. The second is the on-site interaction: it penalizes double occupancy for $U_i>0$ and favors it for $U_i<0$. The third is the chemical-potential term, with $\\mu_i$ the local potential on site $i$.\n",
    "\n",
    "In this tutorial we restrict to a translation-invariant model, taking the couplings to be uniform across the chain: $t_{h,i}=t_h$, $U_i=U$, and $\\mu_i=\\mu$ for all sites. The Hamiltonian then simplifies to\n",
    "\n",
    "$$\n",
    "H =\n",
    "-t_h \\sum_{i=0}^{L-2}\n",
    "\\sum_{\\sigma \\in \\{\\uparrow,\\downarrow\\}}\n",
    "\\left(\n",
    "c^\\dagger_{i\\sigma}c_{i+1,\\sigma}\n",
    "+ \\mathrm{h.c.}\n",
    "\\right)\n",
    "+\n",
    "U\\sum_{i=0}^{L-1} n_{i\\uparrow}n_{i\\downarrow}\n",
    "-\n",
    "\\mu\\sum_{i=0}^{L-1}\n",
    "\\sum_\\sigma n_{i\\sigma}.\n",
    "$$\n",
    "\n",
    "Throughout this notebook, energy is measured in units of $t_h$ and time in units of $1/t_h$, so the hopping amplitude is set to `t = 1.0`. We use an attractive interaction, $U=-2t_h$. For a uniform chain and an initial state with fixed particle number, a uniform chemical potential contributes only a global phase, so `mu = 0.0`. It becomes physically meaningful for site-dependent potentials or disorder, as in the general form above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f360cfbf",
   "metadata": {},
   "source": [
    "## 2. Initialize the quantum platform\n",
    "\n",
    "Import the required libraries and configure access to the IBM Quantum Platform.\n",
    "\n",
    "Store credentials securely. The cell below reads the token and instance from environment variables when available, while retaining editable placeholders for interactive use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59ac3430",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qctrlcommons.sparse_occ_op import SparseOccupationOperators\n",
    "from qctrlcommons.simulation_types import ModelType, SimulationType\n",
    "import fireopal as fo\n",
    "import qctrlvisualizer as qv\n",
    "from qctrlvisualizer import QCTRL_STYLE_COLORS as colors\n",
    "\n",
    "plt.style.use(qv.get_qctrl_style())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf7126cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-CTRL authentication successful!\n"
     ]
    }
   ],
   "source": [
    "# Authenticate your Q-CTRL account.\n",
    "api_key = \"YOUR_QCTRL_API_KEY\"\n",
    "fo.authenticate_qctrl_account(api_key=api_key)\n",
    "\n",
    "token = \"YOUR_IBM_CLOUD_API_KEY\"\n",
    "instance = \"YOUR_IBM_CRN\"\n",
    "\n",
    "credentials = fo.credentials.make_credentials_for_ibm_cloud(\n",
    "    token=token, instance=instance\n",
    ")\n",
    "\n",
    "# Replace with a backend available to your IBM Quantum instance.\n",
    "backend_name = \"desired_backend\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91267fc7",
   "metadata": {},
   "source": [
    "## 3. Define model and Trotter parameters\n",
    "\n",
    "Configure a 10-site 1D Fermi-Hubbard chain with attractive on-site interaction $U=-2.0$ and hopping amplitude $t=1.0$. The simulation uses 10 Trotter steps with step size $\\mathrm{d}t=0.2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4fa45bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating 10-site 1D Fermi-Hubbard: t=1.0, U=-2.0, mu=0.0\n",
      "Trotter: 10 steps, dt=0.2 -> total time = 2.0\n"
     ]
    }
   ],
   "source": [
    "# Physical parameters\n",
    "L = 10  # Number of lattice sites\n",
    "t = 1.0  # Hopping amplitude\n",
    "U = -2.0  # On-site interaction (attractive)\n",
    "mu = 0.0  # Chemical potential\n",
    "\n",
    "# Algorithmic Trotter parameters\n",
    "dt = 0.2  # Time-step size\n",
    "n_steps = 10  # Number of Trotter steps\n",
    "\n",
    "hopping_amplitudes = [float(t)] * (L - 1)\n",
    "on_site_interactions = [float(U)] * L\n",
    "chemical_potentials = [float(mu)] * L\n",
    "\n",
    "# Build the model\n",
    "model = ModelType(\n",
    "    hopping_amplitude=hopping_amplitudes,\n",
    "    on_site_interaction=on_site_interactions,\n",
    "    chemical_potential=chemical_potentials,\n",
    ")\n",
    "\n",
    "# Define the Trotter schedule.\n",
    "simulation = SimulationType(time_step=float(dt), step_count=int(n_steps))\n",
    "\n",
    "print(f\"Simulating {L}-site 1D Fermi-Hubbard: t={t}, U={U}, mu={mu}\")\n",
    "print(f\"Trotter: {n_steps} steps, dt={dt} -> total time = {dt * n_steps}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afa10bf8",
   "metadata": {},
   "source": [
    "## 4. Prepare the initial state and observables\n",
    "\n",
    "### 4.1 Encode the initial state\n",
    "\n",
    "Initialize the system in a half-filled Néel product state with alternating spin-up (`u`) and spin-down (`d`) occupations. Each physical site contains two spin orbitals and therefore maps to two qubits, so an `L`-site chain requires `2L` qubits.\n",
    "\n",
    "The initial state is supplied as a Fock-state occupation string containing one character for each physical site. The four supported characters specify the spin-up and spin-down occupations at a site as follows:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\texttt{e} &\\longleftrightarrow\n",
    "\\left(n_{i,\\uparrow},n_{i,\\downarrow}\\right)=(0,0),\n",
    "\\\\\n",
    "\\texttt{u} &\\longleftrightarrow\n",
    "\\left(n_{i,\\uparrow},n_{i,\\downarrow}\\right)=(1,0),\n",
    "\\\\\n",
    "\\texttt{d} &\\longleftrightarrow\n",
    "\\left(n_{i,\\uparrow},n_{i,\\downarrow}\\right)=(0,1),\n",
    "\\\\\n",
    "\\texttt{b} &\\longleftrightarrow\n",
    "\\left(n_{i,\\uparrow},n_{i,\\downarrow}\\right)=(1,1).\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Here, `e` denotes an empty site, `u` and `d` denote singly occupied sites with spin up and spin down, respectively, and `b` denotes a doubly occupied site.\n",
    "\n",
    "The occupation string follows a least-significant-site-first convention: the rightmost character corresponds to site 0, the next character to site 1, and so on. If `k` is the character position counted from the left, beginning at 0, then its corresponding physical-site index is\n",
    "\n",
    "$$\n",
    "i=L-1-k.\n",
    "$$\n",
    "\n",
    "For example, the string `udud` is interpreted in increasing site order as\n",
    "\n",
    "$$\n",
    "\\left\\{\n",
    "0:\\texttt{d},\n",
    "\\;\n",
    "1:\\texttt{u},\n",
    "\\;\n",
    "2:\\texttt{d},\n",
    "\\;\n",
    "3:\\texttt{u}\n",
    "\\right\\}.\n",
    "$$\n",
    "\n",
    "Because each site has four possible local occupation states, an $L$-site system has $4^L = 2^{2L}$ distinct occupation strings. This equals the number of computational-basis states of the corresponding $2L$-qubit system, so these strings enumerate the complete Fock basis. A single occupation string specifies one Fock-basis product state, while a general many-body state may be a superposition of these basis states.\n",
    "\n",
    "### 4.2 Construct occupation observables\n",
    "\n",
    "For each lattice site $i$, you measure the two spin-resolved occupation operators: the spin-up occupation $n_{i,\\uparrow}$ and the spin-down occupation $n_{i,\\downarrow}$. Their expectation values can be added to obtain the total occupation of site $i$:\n",
    "\n",
    "$$\n",
    "\\langle n_i \\rangle\n",
    "=\n",
    "\\langle n_{i,\\uparrow} \\rangle\n",
    "+\n",
    "\\langle n_{i,\\downarrow} \\rangle.\n",
    "$$\n",
    "\n",
    "The `observables` argument passed to `simulate_dynamics` is a list of `SparseOccupationOperators` objects. Each `SparseOccupationOperators` object defines a single observable as a weighted sum of products of occupation-number operators:\n",
    "\n",
    "$$\n",
    "O\n",
    "=\n",
    "\\sum_{\\alpha} c_{\\alpha}\n",
    "\\prod_{(\\sigma,j)\\in T_{\\alpha}} n_{j,\\sigma}.\n",
    "$$\n",
    "\n",
    "Here, each element of `operators` specifies one term $T_{\\alpha}$, and the corresponding element of `coefficients` specifies its coefficient $c_{\\alpha}$. Within a term, the listed occupation operators are multiplied together.\n",
    "\n",
    "For example, a one-body observable can be defined as\n",
    "\n",
    "```python\n",
    "SparseOccupationOperators(\n",
    "    operators=[[(\"Nup\", 3)]],\n",
    "    coefficients=[1.0],\n",
    ")\n",
    "```\n",
    "\n",
    "which represents\n",
    "\n",
    "$$\n",
    "n_{3,\\uparrow}.\n",
    "$$\n",
    "\n",
    "A single observable may also contain multiple terms, including multi-point occupation operators. For example,\n",
    "\n",
    "```python\n",
    "SparseOccupationOperators(\n",
    "    operators=[\n",
    "        [(\"Nup\", 3)],\n",
    "        [(\"Ndn\", 7), (\"Nup\", 9), (\"Nup\", 4)],\n",
    "    ],\n",
    "    coefficients=[1.0, -1.0],\n",
    ")\n",
    "```\n",
    "\n",
    "represents\n",
    "\n",
    "$$\n",
    "n_{3,\\uparrow}\n",
    "-\n",
    "n_{7,\\downarrow}n_{9,\\uparrow}n_{4,\\uparrow}.\n",
    "$$\n",
    "\n",
    "Occupation-number operators are diagonal in the Fock basis and commute with one another:\n",
    "\n",
    "$$\n",
    "\\left[\n",
    "n_{i,\\sigma},\n",
    "n_{j,\\sigma'}\n",
    "\\right]\n",
    "=\n",
    "0.\n",
    "$$\n",
    "\n",
    "Therefore, the ordering of the factors within a multi-point term is irrelevant. However, the ordering of the objects in the top-level `observables` list remains important because it determines the ordering of the expectation values returned by the simulation.\n",
    "\n",
    "Fire Opal internally maps these occupation-number observables to the corresponding Pauli-$Z$ observables in the qubit representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d174242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial state: ududududud\n",
      "Qubits required: 20\n",
      "Built 20 site-occupation observables.\n"
     ]
    }
   ],
   "source": [
    "# Generate the initial-state string, for example \"ududududud\".\n",
    "initial_state = \"\".join(\"u\" if i % 2 == 0 else \"d\" for i in range(L))\n",
    "\n",
    "print(f\"Initial state: {initial_state}\")\n",
    "print(f\"Qubits required: {2 * L}\")\n",
    "\n",
    "# Build local occupation observables for every lattice site.\n",
    "operators = []\n",
    "\n",
    "for site in range(L):\n",
    "    operators.append([(\"Nup\", site)])\n",
    "    operators.append([(\"Ndn\", site)])\n",
    "\n",
    "observables = [\n",
    "    SparseOccupationOperators(operators=[operators[i]], coefficients=[1.0])\n",
    "    for i in range(len(operators))\n",
    "]\n",
    "\n",
    "\n",
    "print(f\"Built {len(observables)} site-occupation observables.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d85080",
   "metadata": {},
   "source": [
    "## 5. Run the managed simulation workflow\n",
    "\n",
    "The managed workflow wraps compilation, runtime error suppression, hardware execution, and post-processing into a unified runner configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a19a75d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = fo.simulate_dynamics(\n",
    "    model=model,\n",
    "    initial_state=initial_state,\n",
    "    simulation=simulation,\n",
    "    observables=observables,\n",
    "    shot_count=4096,\n",
    "    backend_name=backend_name,\n",
    "    credentials=credentials,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eeffb33e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation completed.\n",
      "Provider job IDs: ['d9d9mu4inv1c73aoomk0']\n"
     ]
    }
   ],
   "source": [
    "print(\"Simulation completed.\")\n",
    "print(\"Provider job IDs:\", result.result()[\"provider_job_ids\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd7db09b",
   "metadata": {},
   "source": [
    "## 6. Retrieve results and visualize the dynamics\n",
    "Each panel shows the spin-resolved occupation of one lattice site across the 10 Trotter steps, with $\\langle n_{i,\\uparrow}\\rangle$ in purple and $\\langle n_{i,\\downarrow}\\rangle$ in red. At step 0 every site is fully polarized, one spin orbital occupied and the other empty, with the polarization alternating from site to site. This is the half-filled Néel state you prepared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7757f37e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points: 11 (Trotter steps 0 through 10)\n",
      "Occupation range: [0.001, 0.996]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x846 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_expectation_values = result.result()[\"observable_expectation_values\"]\n",
    "\n",
    "n_data_points = len(result_expectation_values[0])\n",
    "completed_steps = n_data_points - 1\n",
    "\n",
    "occ = np.asarray(result_expectation_values, dtype=float).reshape(L, 2, n_data_points)\n",
    "\n",
    "print(f\"Data points: {n_data_points} \" f\"(Trotter steps 0 through {completed_steps})\")\n",
    "print(f\"Occupation range: [{occ.min():.3f}, {occ.max():.3f}]\")\n",
    "\n",
    "trotter_steps = np.arange(n_data_points)\n",
    "\n",
    "sites_to_plot = list(range(L))\n",
    "n_columns = 2\n",
    "n_rows = int(np.ceil(len(sites_to_plot) / n_columns))\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    nrows=n_rows, ncols=n_columns, figsize=(12, 2.35 * n_rows), sharex=True, sharey=True\n",
    ")\n",
    "\n",
    "axes = np.atleast_1d(axes).ravel()\n",
    "\n",
    "spin_up_line = None\n",
    "spin_down_line = None\n",
    "\n",
    "for panel_index, site in enumerate(sites_to_plot):\n",
    "    ax = axes[panel_index]\n",
    "\n",
    "    (spin_up_line,) = ax.plot(\n",
    "        trotter_steps,\n",
    "        occ[site, 0],\n",
    "        color=colors[0],\n",
    "        marker=\"o\",\n",
    "        markersize=4.5,\n",
    "        linewidth=2.0,\n",
    "        label=r\"$\\langle n_{i,\\uparrow}\\rangle$\",\n",
    "    )\n",
    "\n",
    "    (spin_down_line,) = ax.plot(\n",
    "        trotter_steps,\n",
    "        occ[site, 1],\n",
    "        color=colors[1],\n",
    "        marker=\"s\",\n",
    "        markersize=4.2,\n",
    "        linewidth=2.0,\n",
    "        label=r\"$\\langle n_{i,\\downarrow}\\rangle$\",\n",
    "    )\n",
    "\n",
    "    ax.set_title(f\"Site {site}\")\n",
    "    ax.set_xlim(trotter_steps[0], trotter_steps[-1])\n",
    "    ax.set_ylim(-0.05, 1.05)\n",
    "    ax.set_xticks(trotter_steps)\n",
    "\n",
    "for ax in axes[len(sites_to_plot) :]:\n",
    "    ax.set_visible(False)\n",
    "\n",
    "fig.suptitle(rf\"Spin-resolved site occupations, $U/t_h={U / t:.1f}$\", y=0.995)\n",
    "\n",
    "fig.supxlabel(\"Trotter step\")\n",
    "fig.supylabel(\"Occupation\")\n",
    "\n",
    "fig.legend(\n",
    "    handles=[spin_up_line, spin_down_line],\n",
    "    labels=[r\"$\\langle n_{i,\\uparrow}\\rangle$\", r\"$\\langle n_{i,\\downarrow}\\rangle$\"],\n",
    "    loc=\"upper center\",\n",
    "    bbox_to_anchor=(0.5, 0.965),\n",
    "    ncol=2,\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "fig.tight_layout(rect=(0.03, 0.03, 1.0, 0.92))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c897eeee",
   "metadata": {},
   "source": [
    "As the evolution proceeds, hopping delocalizes the fermions and the attractive interaction correlates them, so the initial order relaxes toward half-filling and the two spin curves on each site cross and move toward $0.5$. Particle number is conserved, so the pair on every site stays a near-mirror image about $0.5$ and the total occupation holds at roughly one fermion per site throughout. Most sites reach $\\langle n\\rangle \\approx 0.5$ within a few steps, while a few keep mild damped oscillations and a small residual polarization out to step 10, consistent with the finite length of the chain. The smooth, low-noise curves and the clean spin symmetry reflect Fire Opal's error suppression running on hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e836335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Package               | Version |\n",
      "| --------------------- | ------- |\n",
      "| Python                | 3.12.11 |\n",
      "| matplotlib            | 3.10.3  |\n",
      "| networkx              | 3.2.1   |\n",
      "| numpy                 | 2.5.1   |\n",
      "| sympy                 | 1.14.0  |\n",
      "| fire-opal             | 11.2.0  |\n",
      "| qctrl-visualizer      | 9.0.0   |\n",
      "| qctrl-workflow-client | 9.0.1   |\n"
     ]
    }
   ],
   "source": [
    "from fireopal import print_package_versions\n",
    "\n",
    "print_package_versions()"
   ]
  }
 ],
 "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}