{
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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "731yWoNPwepZ",
        "outputId": "0f907a22-00f0-4480-bc51-300caf1f6557"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: appdirs, scipy-openblas32, rustworkx, autoray, diastatic-malt, pennylane-lightning, pennylane\n",
            "Successfully installed appdirs-1.4.4 autoray-0.8.2 diastatic-malt-2.15.2 pennylane-0.44.1 pennylane-lightning-0.44.0 rustworkx-0.17.1 scipy-openblas32-0.3.31.188.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 1. Configuration: 8 qubits (4 per track segment), 3 QAOA layers\n",
        "## Task 1: define number of qubits needed\n",
        "n_qubits = ...\n",
        "n_layers = 3\n",
        "dev = qml.device(\"default.qubit\", wires=n_qubits)\n",
        "\n",
        "# 2. Geometry Setup\n",
        "z_vals = np.array([1, 2, 3, 4])\n",
        "hits_spatial = np.zeros((8, 2))\n",
        "hits_spatial[:4, 0] = z_vals\n",
        "hits_spatial[:4, 1] = 0.5 * z_vals + 1    # Track A: positive slope\n",
        "hits_spatial[4:, 0] = z_vals\n",
        "hits_spatial[4:, 1] = -0.5 * z_vals - 1   # Track B: negative slope\n",
        "\n",
        "# 3. Hamiltonian Construction\n",
        "H_cost = 0.0 * qml.Identity(0)\n",
        "\n",
        "for i in range(n_qubits):\n",
        "    for j in range(i + 1, n_qubits):\n",
        "        # A. Local Rewards: Connect adjacent hits in a track\n",
        "        if (i < 4 and j < 4 and j == i + 1) or (i >= 4 and j >= 4 and j == i + 1):\n",
        "            H_cost += -12.0 * (qml.PauliZ(i) @ qml.PauliZ(j))\n",
        "\n",
        "        # B. Non-Local Rewards: Connect first and last hits to break degeneracy\n",
        "        elif (i == 0 and j == 3) or (i == 4 and j == 7):\n",
        "            H_cost += -8.0 * (qml.PauliZ(i) @ qml.PauliZ(j))\n",
        "\n",
        "        # C. Mutual Exclusion: Penalize jumping between Track A and Track B\n",
        "        else:\n",
        "            H_cost += 3.0 * (qml.PauliZ(i) @ qml.PauliZ(j))\n",
        "\n",
        "# D. Activation Bias (Chemical Potential)\n",
        "# Positive coefficient for Z makes the |1> state (Z=-1) energetically favorable\n",
        "for i in range(n_qubits):\n",
        "    H_cost += 6.0 * qml.PauliZ(i)\n",
        "\n",
        "# Mixer: Standard transverse field\n",
        "## Task 2: implement mixing Hamiltonian with Pauli X gates in all the qubits\n",
        "H_mixer = ...\n",
        "\n",
        "# 4. QAOA Circuit\n",
        "def qaoa_layer(gamma, alpha):\n",
        "  ## Task 3: define cost layers, mixing layers\n",
        "    qml.qaoa.cost_layer(...)\n",
        "    qml.qaoa.mixer_layer(...)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def tracking_circuit(params):\n",
        "    for i in range(n_qubits):\n",
        "        qml.Hadamard(wires=i)\n",
        "    qml.layer(qaoa_layer, n_layers, params[0], params[1])\n",
        "    return qml.expval(H_cost)\n",
        "\n",
        "# 5. Optimization Loop\n",
        "params = np.array([[0.5]*n_layers, [0.5]*n_layers], requires_grad=True)\n",
        "optimizer = qml.AdamOptimizer(stepsize=0.15)\n",
        "\n",
        "print(\"Starting QAOA Optimization for Track Reconstruction...\")\n",
        "for i in range(81):\n",
        "    params = optimizer.step(tracking_circuit, params)\n",
        "    if i % 20 == 0:\n",
        "        print(f\"Iteration {i:2d} | Energy: {tracking_circuit(params):.4f}\")\n",
        "\n",
        "# 6. Extraction & Visualization\n",
        "@qml.qnode(dev)\n",
        "def get_probs(params):\n",
        "    for i in range(n_qubits):\n",
        "        qml.Hadamard(wires=i)\n",
        "    qml.layer(qaoa_layer, n_layers, params[0], params[1])\n",
        "    return qml.probs(wires=range(n_qubits))\n",
        "\n",
        "probs = get_probs(params)\n",
        "best_bitstring = format(np.argmax(probs), f'0{n_qubits}b')\n",
        "\n",
        "def plot_final_reconstruction(bitstring, coords):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.scatter(coords[:, 0], coords[:, 1], c='lightgray', s=200, label='Detector Hits')\n",
        "\n",
        "    # Track A Logic (Qubits 0-3)\n",
        "    ta = [coords[i] for i in range(4) if bitstring[i] == '1']\n",
        "    if ta:\n",
        "        ta = np.array(ta)\n",
        "        plt.plot(ta[:, 0], ta[:, 1], 'bo-', markersize=12, linewidth=2.5, label='Track A (Reco)')\n",
        "\n",
        "    # Track B Logic (Qubits 4-7)\n",
        "    tb = [coords[i] for i in range(4, 8) if bitstring[i] == '1']\n",
        "    if tb:\n",
        "        tb = np.array(tb)\n",
        "        plt.plot(tb[:, 0], tb[:, 1], 'ro-', markersize=12, linewidth=2.5, label='Track B (Reco)')\n",
        "\n",
        "    plt.title(f\"Final QAOA Solution: |{bitstring}>\", fontsize=14)\n",
        "    plt.xlabel(\"z-axis (Detector Layers)\", fontsize=12)\n",
        "    plt.ylabel(\"r-axis (Transverse Plane)\", fontsize=12)\n",
        "    plt.legend()\n",
        "    plt.grid(alpha=0.3)\n",
        "    plt.show()\n",
        "\n",
        "plot_final_reconstruction(best_bitstring, hits_spatial)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "jnAYl4ptzxbl",
        "outputId": "5b10fda7-891c-4467-f266-fd54d900987d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting QAOA Optimization for 8-qubit Track Reconstruction...\n",
            "Iteration  0 | Energy: 4.9084\n",
            "Iteration 20 | Energy: -17.6753\n",
            "Iteration 40 | Energy: -29.2583\n",
            "Iteration 60 | Energy: -35.4337\n",
            "Iteration 80 | Energy: -36.7818\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}