{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6cf1c4f8",
   "metadata": {},
   "source": [
    "# ErrorChannelV0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb1c6a5",
   "metadata": {},
   "source": [
    "![image](../images/error_channelv0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4c9259",
   "metadata": {},
   "source": [
    "## Description\n",
    "\n",
    "The **ErrorChannelV0** environment simulates a **multi-qubit noisy quantum system** where different qubits\n",
    "may be subject to error channels (bitflip errors) with varying probabilities. It is based on the `QuantumEnv` base class. \n",
    "The agent's task is to identify and apply corrective single-qubit gates to mitigate the effects of these noise processes and restore the system state\n",
    "to the computational basis state **|0...0⟩**.\n",
    "\n",
    "This environment models the challenge of **quantum error mitigation/correction** in the presence of **Bit-flip errors**.\n",
    "\n",
    "The agent interacts by applying X gate to specific qubits. Rewards are based\n",
    "on the probability that the corrected quantum state has returned to the target basis state.\n",
    "\n",
    "The environment includes a rendering mode that provides a **bar plot animation** comparing the noisy,\n",
    "corrected, and ideal probability distributions, along with the evolving circuit diagram.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d10643",
   "metadata": {},
   "source": [
    "## Action Space\n",
    "\n",
    "The action space is a **Discrete( [n_qubits] )** space, meaning the agent chooses:\n",
    "\n",
    "1. A **target qubit**: index in `[0, n_qubits-1]` to apply the PauliX correction on.\n",
    "\n",
    "Example (for 3 qubits):  \n",
    "- `0` → Apply `X` gate on qubit 0  \n",
    "- `2` → Apply `X` gate on qubit 2  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9174d2e4",
   "metadata": {},
   "source": [
    "## Observation Space\n",
    "\n",
    "The observation is a probability distribution over all computational basis states\n",
    "of the `n_qubits` system:\n",
    "\n",
    "obs $\\in [0, 1]^{2^{n_{qubits}}}$\n",
    "\n",
    "with the constraint:\n",
    "\n",
    "$\\sum_{i} obs_i = 1$\n",
    "\n",
    "For example, with 3 qubits, the observation is a length-8 vector:\n",
    "\n",
    "| Index | Basis State | Probability |\n",
    "|-------|-------------|-------------|\n",
    "| 0     |  \\|000>       | [0, 1]      |\n",
    "| 1     |  \\|001>       | [0, 1]      |\n",
    "| ...   |  ...          | ...         |\n",
    "| 7     |  \\|111>       | [0, 1]      |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f284396f",
   "metadata": {},
   "source": [
    "## Rewards\n",
    "\n",
    "The reward is the mean squared error between the target state and the corrected state multiplied by -1:\n",
    "\n",
    "reward = -MSE(|0...0⟩, |corrected_state⟩)\n",
    "\n",
    "- Maximum reward = 0.0 (perfect correction).  \n",
    "- Minimum reward = -(2/2**num_qubits) (highly corrupted)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccbc113e",
   "metadata": {},
   "source": [
    "## Starting State\n",
    "\n",
    "At the start of each episode:\n",
    "- A set of **faulty qubits** is defined to apply bit flip errors with varying probabilities.  \n",
    "(If not specified, a random qubit is chosen.)\n",
    "- The agent begins with no corrections applied.\n",
    "\n",
    "The first observation is the noisy probability distribution before any corrections.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35f1a61",
   "metadata": {},
   "source": [
    "## Episode End\n",
    "\n",
    "The episode ends if one of the following occurs:\n",
    "\n",
    "1. **Termination**:  \n",
    "The obtained reward is 0.0.\n",
    "2. **Truncation**:  \n",
    "The system reaches the maximum number of steps (`max_steps`, default=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c2ca8f",
   "metadata": {},
   "source": [
    "## Rendering\n",
    "\n",
    "The rendering shows a **side-by-side animation** with:\n",
    "\n",
    "1. **Left panel**: Bar chart of probabilities for each basis state:  \n",
    "- Gray = Ideal noiseless target (|0...0⟩)  \n",
    "- Orange = Noisy distribution  \n",
    "- Blue = Corrected distribution  \n",
    "\n",
    "The chart title updates with **step number, chosen correction, and reward**.\n",
    "\n",
    "2. **Right panel**: A dynamically drawn ASCII-style **quantum circuit** reflecting\n",
    "the applied corrections.\n",
    "\n",
    "The animation can be displayed interactively or saved as an MP4 file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6757f2c9",
   "metadata": {},
   "source": [
    "## Arguments\n",
    "\n",
    "- **`n_qubits`** (`int`, default=3): Number of qubits in the system.  \n",
    "- **`faulty_qubits`** (`dict`, optional): Mapping of faulty qubit indices to their noise probabilities.  \n",
    "- **`max_steps`** (`int`, default=10): Maximum number of agent actions per episode.  \n",
    "- **`seed`** (`int`, optional): Random seed for reproducibility.  \n",
    "- **`ffmpeg`** (`bool`, default=False): If `True`, uses FFmpeg for saving animations; otherwise uses Pillow (GIF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "253e93a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step=1 act=0 reward=-0.250 errors={0: 1.0, 2: 1.0}\n",
      "step=2 act=1 reward=-0.250 errors={0: 1.0, 2: 1.0}\n",
      "step=3 act=2 reward=-0.250 errors={0: 1.0, 2: 1.0}\n",
      "step=4 act=0 reward=-0.250 errors={0: 1.0, 2: 1.0}\n",
      "step=5 act=1 reward=-0.250 errors={0: 1.0, 2: 1.0}\n",
      "step=6 act=0 reward=-0.000 errors={0: 1.0, 2: 1.0}\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qrl.env import ErrorChannelV0\n",
    "\n",
    "# set ffmpeg=True if you have ffmpeg installed to save as mp4, or ffmpeg=False to save as gif\n",
    "env = ErrorChannelV0(\n",
    "    n_qubits=3,\n",
    "    faulty_qubits={0: 1.0, 2: 1.0},\n",
    "    max_steps=6,\n",
    "    seed=42,\n",
    "    ffmpeg=False\n",
    ")\n",
    "obs = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    action = env.action_space.sample()\n",
    "    obs, reward, done, info = env.step(action)\n",
    "    print(f\"step={len(env.corrections)} act={action} \"\n",
    "            f\"reward={reward:.3f} errors={info['faulty_qubits']}\")\n",
    "env.render(save_path_without_extension=\"error_channelV0\", interval_ms=700)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qrl_env (3.12.3)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}