autoresearch-quantum/notebooks/plan_b/spiral_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spiral Notebook: Three Passes Through Quantum Autoresearch\n",
    "\n",
    "We are going to run an automated quantum experiment optimizer. **First time through, you just watch.** Second time, you will understand what you are watching. Third time, you will drive.\n",
    "\n",
    "Each pass covers the *entire* system at increasing depth -- like zooming into a fractal where each pass reveals structure that was invisible before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Pass 1: The 5-Minute Demo\n",
    "\n",
    "You do not need to understand anything yet. The goal is: **run the machine, see it work, get curious.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import tempfile\n",
    "import json\n",
    "from dataclasses import asdict\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from autoresearch_quantum.config import load_rung_config\n",
    "from autoresearch_quantum.ratchet.runner import AutoresearchHarness\n",
    "from autoresearch_quantum.persistence.store import ResearchStore"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "from autoresearch_quantum.teaching import LearningTracker\n",
    "from autoresearch_quantum.teaching.assess import quiz, predict_choice, reflect, order, checkpoint_summary\n",
    "tracker = LearningTracker(\"plan_b_spiral\")\n",
    "print(\"Learning tracker active.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Load the Experiment Configuration\n",
    "\n",
    "We load a pre-built configuration for rung 1. Don't worry about what the fields mean yet -- just notice it has a **name** and an **objective**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:      [[4,2,2]] Encoded Magic-State Preparation\n",
      "Objective: Maximize acceptance-weighted encoded magic quality for [[4,2,2]] T-state preparation on a backend-aware cheap tier.\n"
     ]
    }
   ],
   "source": [
    "rung1_config = load_rung_config(\"../../configs/rungs/rung1.yaml\")\n",
    "print(f\"Name:      {rung1_config.name}\")\n",
    "print(f\"Objective: {rung1_config.objective}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Run a Single Ratchet Step\n",
    "\n",
    "This runs the entire optimization loop once: evaluate the incumbent, generate challengers, compare, pick a winner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"step_index\": 1,\n",
      "  \"rung\": 1,\n",
      "  \"incumbent_before_id\": \"r1-incumbent-4343a2eac0\",\n",
      "  \"challengers_tested\": [\n",
      "    \"r1-challenger-2bc9c87de9\",\n",
      "    \"r1-challenger-6a036b7f5f\",\n",
      "    \"r1-challenger-3b16c89d93\",\n",
      "    \"r1-challenger-7b5e8bbe50\",\n",
      "    \"r1-challenger-581df4774d\",\n",
      "    \"r1-challenger-6f76d2ff9b\",\n",
      "    \"r1-challenger-0006429bd3\",\n",
      "    \"r1-challenger-610e9daff6\"\n",
      "  ],\n",
      "  \"promoted_challengers\": [\n",
      "    \"r1-challenger-581df4774d\",\n",
      "    \"r1-challenger-7b5e8bbe50\"\n",
      "  ],\n",
      "  \"winner_id\": \"r1-challenger-581df4774d\",\n",
      "  \"winning_margin\": 0.027819483580358982,\n",
      "  \"cheap_tier_justification\": \"Promoted challengers beat the incumbent on cheap-tier score by at least 0.0020.\",\n",
      "  \"expensive_tier_result\": \"Hardware tier disabled.\",\n",
      "  \"distilled_lesson\": \"verification: both -> z_only, ancilla_strategy: dedicated_pair -> reused_single became the new incumbent on cheap-tier score. It improved final score by +0.0278; 2 challengers were strong enough to justify promotion.\",\n",
      "  \"created_at\": \"2026-04-05T17:30:33.643541+00:00\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "store_dir = tempfile.mkdtemp()\n",
    "store = ResearchStore(store_dir)\n",
    "harness = AutoresearchHarness(store)\n",
    "\n",
    "step = harness.run_ratchet_step(rung1_config)\n",
    "print(json.dumps(asdict(step), indent=2, default=str))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Key Numbers at a Glance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winner:             r1-challenger-581df4774d\n",
      "Winning margin:     +0.027819\n",
      "Challengers tested: 8\n",
      "Promoted:           2\n",
      "\n",
      "Lesson: verification: both -> z_only, ancilla_strategy: dedicated_pair -> reused_single became the new incumbent on cheap-tier score. It improved final score by +0.0278; 2 challengers were strong enough to justify promotion.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Winner:             {step.winner_id}\")\n",
    "print(f\"Winning margin:     {step.winning_margin:+.6f}\")\n",
    "print(f\"Challengers tested: {len(step.challengers_tested)}\")\n",
    "print(f\"Promoted:           {len(step.promoted_challengers)}\")\n",
    "print(f\"\\nLesson: {step.distilled_lesson}\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "quiz(tracker, \"p1_q1_what_is_score\",\n",
    "    question=\"The winning margin tells you how much the winner improved. What does a margin of 0.0 mean?\",\n",
    "    options=[\n",
    "        \"The experiment failed\",\n",
    "        \"No challenger beat the incumbent \\u2014 the incumbent stayed\",\n",
    "        \"All challengers tied exactly\",\n",
    "    ],\n",
    "    correct=1, section=\"Pass 1: Demo\", bloom=\"remember\",\n",
    "    explanation=\"Margin 0.0 means the incumbent was not replaced. The ratchet guarantee: never worse.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Run a Full Rung\n",
    "\n",
    "A rung runs multiple ratchet steps (up to the `step_budget`), with a patience mechanism that stops early if no improvement is found. At the end, it produces a **lesson** -- a structured summary of what the ratchet learned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steps completed: 3\n",
      "Step budget:     3\n",
      "Patience:        2\n"
     ]
    }
   ],
   "source": [
    "# Fresh store for the full rung\n",
    "store_rung = ResearchStore(tempfile.mkdtemp())\n",
    "harness_rung = AutoresearchHarness(store_rung)\n",
    "\n",
    "steps, lesson, feedback = harness_rung.run_rung(rung1_config)\n",
    "print(f\"Steps completed: {len(steps)}\")\n",
    "print(f\"Step budget:     {rung1_config.step_budget}\")\n",
    "print(f\"Patience:        {rung1_config.patience}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 The Lesson Narrative\n",
    "\n",
    "This is a human-readable summary of what the ratchet discovered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Rung 1: [[4,2,2]] Encoded Magic-State Preparation\n",
       "\n",
       "Objective: Maximize acceptance-weighted encoded magic quality for [[4,2,2]] T-state preparation on a backend-aware cheap tier.\n",
       "\n",
       "## What Helped\n",
       "- verification=z_only improved mean score by +0.0074 over 17 runs.\n",
       "- optimization_level=3 improved mean score by +0.0068 over 8 runs.\n",
       "- ancilla_strategy=reused_single improved mean score by +0.0037 over 9 runs.\n",
       "\n",
       "## What Hurt\n",
       "- verification=both hurt mean score by -0.0157 over 8 runs.\n",
       "- optimization_level=1 hurt mean score by -0.0092 over 4 runs.\n",
       "- ancilla_strategy=dedicated_pair hurt mean score by -0.0021 over 16 runs.\n",
       "\n",
       "## Invariants\n",
       "- Top-ranked experiments consistently kept encoder_style=cx_chain.\n",
       "- Top-ranked experiments consistently kept verification=z_only.\n",
       "- Top-ranked experiments consistently kept optimization_level=3.\n",
       "\n",
       "## Hardware-Specific Effects\n",
       "- No hardware-specific divergence was observed in this rung.\n",
       "\n",
       "## Next Tests\n",
       "- Probe remaining verification values: ['x_only'].\n",
       "\n",
       "## Promote Forward\n",
       "- seed_style: h_p -> u_magic, verification: both -> z_only, ancilla_strategy: dedicated_pair -> reused_single became the new incumbent on cheap-tier score. It improved final score by +0.0268; 2 challengers were strong enough to justify promotion.\n",
       "- seed_style: u_magic -> h_p, ancilla_strategy: reused_single -> dedicated_pair, optimization_level: 2 -> 3 became the new incumbent on cheap-tier score. It improved final score by +0.0025; 1 challengers were strong enough to justify promotion.\n",
       "- seed_style: h_p -> ry_rz became the new incumbent on cheap-tier score. It improved final score by +0.0046; 1 challengers were strong enough to justify promotion.\n",
       "\n",
       "## Discard\n",
       "- verification=both hurt mean score by -0.0157 over 8 runs.\n",
       "- optimization_level=1 hurt mean score by -0.0092 over 4 runs.\n",
       "- ancilla_strategy=dedicated_pair hurt mean score by -0.0021 over 16 runs."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "display(Markdown(lesson.narrative))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 Score Landscape\n",
    "\n",
    "Let's see how all the experiments the ratchet tried compare to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ratchet found the best settings automatically.\n"
     ]
    }
   ],
   "source": [
    "experiments = store_rung.list_experiments(rung1_config.rung)\n",
    "incumbent_id = store_rung.load_incumbent_id(rung1_config.rung)\n",
    "\n",
    "exp_ids = [e[\"experiment_id\"][:16] for e in experiments]\n",
    "scores = [e[\"final_score\"] for e in experiments]\n",
    "colors = [\"#2ecc71\" if e[\"experiment_id\"] == incumbent_id else \"#3498db\" for e in experiments]\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "bars = plt.bar(range(len(scores)), scores, color=colors)\n",
    "plt.xticks(range(len(scores)), exp_ids, rotation=60, ha=\"right\", fontsize=7)\n",
    "plt.ylabel(\"Score\")\n",
    "plt.title(\"All Experiments Scored by the Ratchet\")\n",
    "plt.legend(\n",
    "    handles=[\n",
    "        plt.Rectangle((0,0),1,1, color=\"#2ecc71\", label=\"Incumbent (winner)\"),\n",
    "        plt.Rectangle((0,0),1,1, color=\"#3498db\", label=\"Challengers\"),\n",
    "    ],\n",
    "    loc=\"upper right\"\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"The ratchet found the best settings automatically.\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "predict_choice(tracker, \"p1_q2_score_spread\",\n",
    "    question=\"Looking at the score landscape: is there a large spread between the best and worst experiments?\",\n",
    "    options=[\n",
    "        \"No \\u2014 all experiments score roughly the same\",\n",
    "        \"Yes \\u2014 there is significant variation, meaning parameter choice matters a lot\",\n",
    "        \"Impossible to tell from a bar chart\",\n",
    "    ],\n",
    "    correct=1, section=\"Pass 1: Demo\", bloom=\"understand\",\n",
    "    explanation=\"Parameter choice strongly affects the score. This is why optimization matters.\")\n",
    "\n",
    "checkpoint_summary(tracker, \"Pass 1: Demo\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions You Should Have Right Now\n",
    "\n",
    "- What is a \"score\"?\n",
    "- What are \"challengers\"?\n",
    "- What is this circuit actually *doing*?\n",
    "- What do \"verification\", \"postselection\", and \"witness\" mean?\n",
    "- How does the ratchet decide one experiment is better than another?\n",
    "\n",
    "**Good -- Pass 2 answers all of these.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Pass 2: Opening the Black Box\n",
    "\n",
    "Now we rewind and build understanding from the ground up, but always connecting back to what we saw in Pass 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 The Quantum State: The Magic T-State\n",
    "\n",
    "A **magic state** is a specific quantum state needed for universal fault-tolerant quantum computation. The T-state is:\n",
    "\n",
    "$$|T\\rangle = \\frac{|0\\rangle + e^{i\\pi/4}|1\\rangle}{\\sqrt{2}}$$\n",
    "\n",
    "This state is \"magic\" because it cannot be created by the cheap Clifford gates alone. If you can prepare it reliably, you unlock the full power of quantum computing. The whole point of this project is to prepare this state *inside an error-detecting code*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-state amplitudes: [0.70710678+0.j  0.5       +0.5j]\n",
      "Expected: [1/sqrt(2), e^(i*pi/4)/sqrt(2)] = [0.7071, 0.5000+0.5000j]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit.visualization import plot_bloch_multivector\n",
    "from math import pi, sqrt\n",
    "\n",
    "# Build |T> on a single qubit\n",
    "qc_t = QuantumCircuit(1)\n",
    "qc_t.h(0)\n",
    "qc_t.p(pi/4, 0)\n",
    "\n",
    "t_state = Statevector.from_instruction(qc_t)\n",
    "print(\"T-state amplitudes:\", t_state.data)\n",
    "print(f\"Expected: [1/sqrt(2), e^(i*pi/4)/sqrt(2)] = [{1/sqrt(2):.4f}, {np.exp(1j*pi/4)/sqrt(2):.4f}]\")\n",
    "\n",
    "plot_bloch_multivector(t_state)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "quiz(tracker, \"p2_q1_tstate\",\n",
    "    question=\"The T-state amplitude on |1\\u27E9 has a specific phase. What is it?\",\n",
    "    options=[\"\\u03C0/2 (90\\u00b0)\", \"\\u03C0/4 (45\\u00b0)\", \"\\u03C0/8 (22.5\\u00b0)\"],\n",
    "    correct=1, section=\"Pass 2: Concepts\", bloom=\"remember\",\n",
    "    explanation=\"The phase is \\u03C0/4 = 45\\u00b0. The gate is called T (for \\u03C0/8) because of Bloch sphere conventions.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** The T-state sits at a specific angle on the Bloch sphere (latitude $\\pi/4$ from the equator in the X-Y plane). Its special position is what makes it \"magic\" -- it lies outside the stabilizer polytope."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 The Encoding: [[4,2,2]] Error-Detecting Code\n",
    "\n",
    "The [[4,2,2]] code uses **4 physical qubits** to encode **2 logical qubits**, with a code distance of 2 (can detect 1 error). We place our magic state on logical qubit 0 and the spectator state $|+\\rangle$ on logical qubit 1.\n",
    "\n",
    "The `build_preparation_circuit()` function creates this encoded state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit qubits: 4\n",
      "Circuit depth:  7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.552x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2GxrrnSe6KCLcfXN6zPwg0+szYrDnJxoDriLMwO8c3bxKEfUvOv7zphWK/N/PB5Z+pEqX3OD8nL11jQ58+4ESBjzi1bbCfR+8U8d3dWrrXOgcEjNkM+bWFlo5ra/qJcZY8ysadE0D/Tj9RnW6+MJXp3VoUUXfv9dXv+t7/DQwgK9jmAl+J3vzqpMBxoSZiu37OHOkDn7/mWre8qRz+5F1C5W7Z4vWDG/k/Dtvf7q2br9TefvTVOWa0ycIw54hp9/0aqBuHRL095dW6Y2PN+pwVp4L20vXda6th+9opQ4tq8pGphfpq1d6acqHG/TM22vPev6ms2lUJ1ajhjTTXf2bKiSE77qwB2EGfuVYppk3EaTylY+f1Th7y49K6P+wjqZ+p4jEJIVEHJ/EaALLqaFlw8NdnKGpipf8MjkYdjKTYic9dKnGjWrrnGxz1oJtWrEuU7szzzxDdFREqFo3rawubas7hfjq1rSnJ+ZcPUt33NRUt/drogUr0vXaf3/Sd2v2av3mgyosLDojwDWpW0HtmlXRzX0aqlv7Gl6rLAxcCMIM/MrRn78/2StjhERV1J45zys0Nv60VUzwfzFR5TV8YJJzMT1zu/Yc1da0I7p+5BfKOJCrqnHh2jV3sN/2QJieqivaJjgXI+tonlI2H9A1wz9z3n+VSmHaNGeAs58A2xFm4FcqtrvOuZyQ9H/LnOu1I5qp2t/nn3W7Jv/4yiPtg/c+2GtWi3IuJwrfmXNS+WuQOdvpLdo2q3Ly/ZtCigQZ+AvCDAJCs0lrvd0EAEAZCZyvJQAAwC8RZgAAgNUIMwAAwGrMmfFVYWEKfe91WSPMvSe7M4VMF14rq7ix+CoAwAWEGR/lnNQuPFyByrz9CP46AQAlwDATAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYjzAAAAKsRZgAAgNUIMwAAwGqEGQAAYDXCDAAAsBphBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgtVBvNwAAgLJUVFSk7elZWpmSoT37cpSXX6iwciGqVT1KbZLjFV8pnF+A5QgzAAC/DDBfLUvTi++v17zv0pSxP+esj62dEKVenWpp+IAktWgc59F2wj0IMwAAvwox73++WY89v1LrNx8s0Tbb0rL0wnvrnUuni6tp/H3t1LFVtTJvK9yHOTMAAL+wOzNbN90/TwPHzC9xkPm1hSt36/JbZmvM/32n7Jx8t7cRZYMwAwCw3op1GWpx44f6cO6WC36uwsIiTXh9tToMmam0vUfd0j6ULcIMAMBqy9bsVddhc7T3HPNiSmP1T/vV6dZZBBoLEGYAANbasvOweg7/TIez8srk+TdtP6yr7/qUIScfR5gBAFjJDAcNe2yh9h3MLfE2y6b20fYvBjnXrvTQPDppRSlbCU8IiDCTkZGhsWPHqmHDhgoPD1etWrU0atQoZWVladiwYQoKCtKkSZO83UwAgAsm/2/ZtSuqx0cqsVqUc+2Kf725Rt+s2s3vx0f5/dLsVatW6ZprrlF6erqioqKUnJysXbt2aeLEidq0aZP27dvnPK5169bebiqAMnIsr0AffrlFsxduV+aB49/izbCEqT1CwTQ7mZVGj3iwt6SoSBr79DItev06j70mSi7Y33tkevfu7QSZ0aNHKy0tTStXrnT+PX78eM2ePVvLli1zemZatmzp7eYCKIOaI/9+a41qXzVNgx/4Sm/N2qScYwXOfYey8pTY413d/thCHTlaNvMtUHamffazS8NL7rD4+936MfX4F2D4Fr8OMyNHjtSOHTs0YsQITZgwQTExMSfvM8NOrVq1Un5+vurWravY2FivthWA+4PMqPFLdO+TS536I8XJPVagKTNS1eW2OTp4+Bi/Aos8Py0loF4XARpmUlJSNG3aNMXHx2vcuHHFPqZNmzbOtQk1J5wIP+3bt1dYWJjTawPAPs+9m6Jn31lX4holv/nT/DJvE9xjT2a2lq3J8MrunL1gu1deFwEaZqZOnarCwkINGTJE0dHRxT4mIiLijDCzceNGffDBB6pevbratWvnsfYCcJ+8vEKNm/KDS9vMWbhD36d45wMSrjHh01t27M5ywhR8i9+GmXnz5jnXXbt2PetjTC/Mr8NM586dnbk1M2fOVPfu3T3QUgDuNvOrrdq1x/XKrebcPPB9K1MyAzZMIcDCzNatW53rOnXqFHu/mSuzePHiM8JMcLDf7hIgYHy6+PgXFVd9sqh028GztqUf8eou35ae5dXXRwAtzTY1ZIzs7OK7A818GrPayUwKrlevXpm2pW3bts4KKsDb0ireLwVXUFp6mhITE+Wv9kX1l8Kau7zdzvR9fr1f/OVvYH9UXynsomLvM8XwzlVDpnp8xMlrUzzvXNIzjqrd4Jln3P7Anx7R3+5b4nK7cW5mesfy5ctVGqH+vFP279/vLMXu2LHjafeZYaQxY8Y4P5sl2WU9ydcEmZ07d5bpawAlElPg9McWFhT4999k4kEpzPXNivKz/Xu/+MvfQM3DZ/39niiKdz6hIcElelxxDh7I1MF9lu47P+W3YcbMdzErmkw9mR49eqhx48bO7aauzNChQ51eGU8VyzPBCvAFaSEhKjTDqSEhSqhZU/7qSPn9OliK7cKL0lXZj/eLv/wNHIyQjpyjN+VcTI+MCTL5BYVKzzj3RN6zPVdcbDlFRNi573zZhXxW+m2YMXVk3nnnHW3fvl3NmjVT06ZNlZOT46xWMhWBTW2Zzz777LT5MmWltN1mgLsldp+qnXuOKqF6gnas8d/5IYeOHFPN7lN15Gi+S9vNeHmUel7+lPyZP/wNTP98s/r/8fgij18rbljoVGZoyfTImCBTq8e7pXr95Qs/UL3EX+qWwfv8drarGQteuHChevXq5ZyPacuWLYqLi9PkyZOdyr+pqanO4zwRZgB4Vmx0eQ27oYlL2yTVr6irLuXbtg3aJMd77bUrxZZX3ZrFl/uA9/htz4yRlJSkWbNmnXH7kSNHnHBjVi41b+76JEEAvu+Je9vq+/WZWrDi/JPvzfmZPnqmu4KDKZJpAxMm6taI1pZdnl/V1LVdDYqp+iC/7Zk5l7Vr1zqlzhs1aqTIyDNnvU+fPt25rFu37rR/M1wE2CM8LFSfPH+1BvY892rFZg0qavHr16lx3QoeaxsujFm08fv+Tb2yG+8a4J3XRQD3zJzN6tWrzznE1L9//2L/fcstt+i1117zQAsBuENkRKjefbKb/jr8gF58f71z1uwDh445t7dNjtfwAUnq1iGBb9oWGnZDYz32/EodyzPTmT2jUZ1YXdmhhsdeDyVHmCmG6bUB4D+a1Kuop8de4lzgH6rERWjE4GT96401HnvNP//+IoYifVRADjOdr2cGAOD7/nZPG6e3xBN6X1FbQ3o18MhrwXUB2TNz4rxNAAB7meHCVx/vrCtum62CgpL1qJ+oHXO+ejSniqsQpsl/vozhSB8WkGEGAOAfLruoml75ayfd8siCEj3+fHVofi0qIlRznrtKCVXOfooEeF9ADjMBAPzHzX0a6fW/d1ZIiHuX1leMKa8vXuqpDi2ruvV54X6EGQCAXwSar6Zcq4a13TOHptPF1bT83evVsVU1tzwfyhZhBgDgFy6/uLp+eP8G3Te0mcqXK93HW4WY8vr3A5foq1d6qUEtz0wuxoVjzgwAwK8mBf9rzCX6022t9MpHqU59oa0lqBR8UdPKuntgkgZfU19RkeU80la4D2EGAOB3qlaO0J+GtdIDt7XUzzsOa8W6DK1MydSz76zV0ZwCRYaHaOzvWjrneTIXJvjajTADAPDrUx+Y4SJzGXB1fb01a6OO5hxVpdgwPTb8Ym83D27CnBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYjzAAAAKsRZgAAgNUIMwAAwGqEGQAAYDXCDAAAsBphBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYjzAAAAKuFersBADyjqKjIuZz4GUDgKSwsUk5ugUJCglS+XLCCgoLkDwgzgJ86llegmfO3aeHKdK1IydSq9ZnKys537tu1N1v1ek5T22bxapMcrxu61VGTehW93WQAbrZp+yF9+OUWLV+XoRXrMrRp++GT90WEh6h1k8rOMeCy1lXVt1sdhYfZGQuCiviKBviVXXuy9Ny7KXr5ww3asy+nxNtd2aGG7hmU5BzQ/OXbGs6U2H2qdu45qppVI7Xjy8EBt4sC4f0XFRVp9oLtznHg08U7Srxd5YphGnZDY90zKFm1E6JlEzsjGIBiD2BTPkzV/ROW6nBWnst7aO7SXc7FhJopf71cdWrEsJcBy+zcnaU7H1+kOQtLHmJOyDyQqydfXe2EoPH3tdPwAUkKDrbjiw0TgAE/sO9grq69+3Pd8ddFpQoypzKBpvmNM/TWrI1uax+Asjf9881qduOHpQoypzLD0SP++a263/GJ9mRmywaEGcByuzOzdcXvZrvUnXw+R47maehDX+uZN9e47TkBlJ2Xpq/XgDHzdPDwMbc95/xlaep062ztSM+SryPMABYzB66r7/pUazbuL5Pnv++ppfrP9PVl8twA3OPt2Rv1+8cXqywWKaZuPagev/9EGftLPv/OGwgzgMVGPvGtftiwr0xf455/fqvVqWX7GgBKJ3XLQd3+l0VluvvWbz74v7DkuyUdCDOApT7+apve+Ni1eS3LpvbR9i8GOdcllZdfqFsfXaC8vMJStBJAWSkoKNTv/rzAqRtT1seBD+du0XufbZavCogwk5GRobFjx6phw4YKDw9XrVq1NGrUKGVlZWnYsGHOMtRJkyZ5u5lAieUeK9Bdf1/s8h6rHh+pxGpRzrUrVqZkatK76/gNAT7k5Q9T9c2qPR47Dtzzz2+c+XS+yO/DzKpVq9SiRQs99dRTSk9PV3JysvLy8jRx4kQNHDhQKSkpzuNat27t7aYCJTb9i83ateeoR/fYs++sc74JAvA+M+Tz77fXevQ1Mw/k6p05m+SLgv29R6Z3795OiBk9erTS0tK0cuVK59/jx4/X7NmztWzZMqdnpmXLlt5uLlBipg6Ep23eeditK6YAlN7Xy9OV8vMBrxx7inxw7oxfh5mRI0dqx44dGjFihCZMmKCYmF+KgJlhp1atWik/P19169ZVbGysV9sKuFKe/NsfXO9adgdX5+gAKBtveqkO1I+p+5yLr/HbMGOGj6ZNm6b4+HiNGzeu2Me0adPGuTah5oTp06erX79+qlOnjiIjI9W0aVM9/PDDOnLkiMfaDpzLd6v3em0HLVuT4bXXBvALjgMBEmamTp2qwsJCDRkyRNHRxZ9jIiIi4owwY3pwQkJC9M9//lOffPKJhg8frhdeeEE9e/Z0ng/wNnPCOG8xQ02ZB3y73gTg77KO5mmdF4aYTjAnrPQ1fntupnnz5jnXXbt2PetjzBDUr8PMxx9/rCpVqpz89xVXXOH824SiRYsWqXPnzmXabuB81pZRgbySWrfpgDq1qe7VNgCBbMOWgyos9N68lbWbvHsMCqgws3XrVufaDBcVx8yVWbx48Rlh5tQgc0Lbtm2d6507d5aqLWZ7M+kYcIe9MbdJ5Yr/uzZ1I8613LJ6fMTJa1Nn4lzSM46q3eCZZ9x+w02DFJ7H3BlbpVW8XwquoLT0NCUmJirQ+MP7zw2tI8XeVqpjgCvHgbMdA5Z8970SE++Su1WvXl3Lly8v1bZ+G2ZMDRkjO7v4k2SZ+TRmtZOZFFyvXr1zPtf8+fOd66SkpFK1xQSZ0gYh4Az1c6Vy564fcT6hIcElelxxMjMypCP8PVsrpsCZYFBYUBCYxyV/eP+RkVLshR0DLuQ4YMqb+Nq+89swYxLe/v37naXYHTt2PO0+s0R7zJgxzs9mSbZZmn025hf26KOPOnNmSluLxrQFcJeMckHKPcc3qXP+LcZHOAew/IJCpWec+2y4Z3uu+MqxCqtQs8TthW9JCwmRmf0XHBKihJqB93v0h/d/LCRWe0t5DHDlOHC25yofWqQqZbDvLuSz0m/DTPfu3Z0VTaaeTI8ePdS4cWPndlNXZujQoU6vjHGugGJWMF1//fUqX768XnnllVK3pbTdZkBx/jhhqf7vjeLPZl1cl/CpTJey+SZmDmC1erxbqh284Yf5iqsQxi/HUondp2rnnqNKqJ6gHWsCr26QP7z/o9n5iun4RrHzZtqd5xjgjuPAsCE99fwjf5Mv8dvVTKaOTOXKlbV9+3Y1a9bMqQLcqFEjtW/fXvXr11e3bt3OmC9zKjM8ZQrubd68WZ9//rkSEhI8/A6A4rVJjvfarqlXM4YgA3hZZESokutXDMhjUMCFGTOxa+HCherVq5dzPqYtW7YoLi5OkydPdir/pqamnjXMmPHAm266yelRMcuzzSkQAF/RoeWZk9Q9pX0L3zuIAYGoQwsvHgeae++1A26Y6cSE3VmzZhU7fGTCTXBwsJo3b37afSdq08ydO1dz5sxxenIAX1I/MVaXXVRNi7/f7fHXvqV3I4+/JoAz3dy7oabMOP6l3JNaN41T80aV5Gv8tmfmXNauXeucW8IMO5kqv6e655579P777+u+++5z7luyZMnJy9693qu8Cpzq7gGlW1l3Ieonxujqy+xcygr4G1PrqVmDil459gSdY9GMtwRkmFm9evVZh5jMsJLxxBNPOKugTr2Y4SnAF/TrUbfUS6tLa+RvkhUc7HsHMSAQmUBx729PH1koa1Uqhes31zaQLyLM/IoZfjK9NsVdbr31Vu/8loBfCSsfopf+fJnH9ku75vG6ZxBzxwBfctsNjdXZg9W4n3/4UkVFnqXIlZcRZgBLXdOpln7X17U5LKZuxI7dWSWqRXFC+XLBeu1vnRUaGpCHC8BnmZ7SVx7vpIjwkDI/Dgy4up5uuurcBWa9ya8nAJ/vvE2A7Z4Ze4l+2LBPK1MyS/T4ktSg+LUXH71MyQ18b8IfAKlBrVi9+nhnDX5gvopKeLomV48DyQ0q6oVHPNcTXBp81QIsFhtdXp++cLVaNYkrk+d/9sGO+l3f4wUnAfimgT3r6+W/dFJZzMtNql9RX0zu6fP1pQgzgOWqxEXoqynX6rrOtdz2nLHR5TR1fBeNGMw8GcCW+TMfPn2lKsWWd9tz9uhYQwte7aUaVT272KA0CDOAH6gYG6aZz/bQ63/vrIoxF3Yw63lZotZ+2E+DrvHNVQsAite3W12t+6ifru9a+4J2UUxUOWeBwWcv9lR8pXArdndAzpkB/HWp5s19Gjm1YF58L0UvfbBBu/aUbIKf6Z42IebugUnq1bmWT9aRAHB+1eMjNeOZ7vr8m5167t0UzVqwrcRzaarGheuOfk00fECSanq49MOFIswAfqZa5Qg9NvxiPXR7a81ZtF2LVu7WinUZ+n59pg4cPuY8JiQkSA0SY9UmubJznpW+3eo4EwkB2C8oKMj5UmMuW3Ye1ox5W51jgLn8tO2QCgqOpxvznaXTxdWd48Blraupd5faKl/OtZVRvoIwA/ipcuWCdX3XOs7lBHOWXXMgM/cB8H91a8bovqGnF9ereeU72rU3WzWqROrrV3vJHxBmgACrS0EVXyCwBfnhMDJfzwAAgNUIMwAAwGqEGQAAYDXCDAAAsBphBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYL9XYDULyioiIpN9ee3RMWpqCgIG+3AgAQgAgzvio3V/kDbpEtQt97XQoP93YzAAABiGEmAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYLiDCTkZGhsWPHqmHDhgoPD1etWrU0atQoZWVladiwYQoKCtKkSZO83UwAAFAKfh9mVq1apRYtWuipp55Senq6kpOTlZeXp4kTJ2rgwIFKSUlxHte6dWtvN7VMfJ2xR+U/fk//2rT+rI8x9/ddutCj7QLgOQcPH9OkqevUZuBHStt71Lltz75sPf3mGu0/lOv3v4qioiJ9uWSnbrzvS6VlZDu3pWdk6/ePL9IPGzIVCLalHdHDE5erce/3tet/fwN79+do2qc/61hegWwXKj/vkendu7cTYkaPHq3HHntMMTExzn1PPvmkHnjgAYWGhjo9My1btvR2cwHA7V79KFV/GPetsrLzT7s9L79I9z+1VA8/u1wT7m+vuwcl++Xe37T9kG6490ut/mn/abcXFBbppekbnEuvzrX09rguqhBTXv4mP79Q909YqufeTVFhYdFp9x3LK9SgsfOVUCVS057sqk5tqstWft0zM3LkSO3YsUMjRozQhAkTTgYZwww7tWrVSvn5+apbt65iY2O92lYAcLfn3l2n2/688Iwgc6rsnALd889v9eQrP/rdL+DnHYd02c2zzggyvzZ7wXZdeccnOnI0T/6ksLBIQx/6Ws++s+6MIHMq01vX/c5PNP+7XbKV34YZM3w0bdo0xcfHa9y4ccU+pk2bNs61CTUnLFy4UN27d1dCQoLCwsKUmJh42nAUANhg+dq9To9MST3wzDItXJEufxpa6nf/XO3OPD6sdD4r1mVo1Pgl8ieTpq7Tu5/+XKLHml6aG++bqwOWDjv6bZiZOnWqCgsLNWTIEEVHRxf7mIiIiDPCzP79+505NmZOzeeff67x48dr7dq16tixo9PLY6ujBQXKyM0t9gLA/0yamqKis38ZL9bEd9bKXyxYka5V6/e5tM1bszYqY3+O/EFhYZHLv88Dh4/pjY83ykZ+O2dm3rx5znXXrl3P+pgT4eTUMNOnTx/ncqp27dqpSZMm+uCDD5xVUDZ6fMNa5wLA/2UeyCnxN/JTzZi3Vbv2ZKlG1SjZ7vlprvemm96JV2akauxt9s+h/OLbndq0/XCp9tsffpPszCW1id+Gma1btzrXderUKfZ+M1dm8eLFZ4SZ4lSuXNm5NpOFS6Nt27bOJGRXRAQHa13rjnKX22vXV78atYq975olX1/w8zdu3FjZhYUX/DwALlxuaB3lxt7m8nYFBUVq2aGXwvPs/HZ+qvQKo6SQOJe3+8v4VzXxz+/LdofDO0uRV7q83YYtB1WzVgMF65g8rXr16lq+fHmptvXbMGNqyBjZ2cWPl5r5NGa1k5kUXK9evTPuLygocIapTCh68MEHnZ08YMCAUrXFBJmdO3e6tE1kSIjkxtXiDaOjdWWVaioru3btcoayAPiA6IpSKdc0ZO47Ih1y7Xjlk2KCpRDXN8vOLXT5eO2TquVIkaXbNG13ppR/SDbx2zBjwoeZ/7Jy5Upnvsup0tLSNGbMGOdnsyS7uO60K6644mTPjSm2Z4atqlSpUuq2uMr0zNikRo0a9MwAPiI3NFoZpdy2cqVIhcfUlO12B+fp7Gu4zi4iTIqraf/7PxxeTqWNIzWqVVKQfln96yml+az0+zBjViSZFUhmAm+PHj2cYRBj2bJlGjp0qNMrc65ieVOmTNGBAwe0efNmp+DeVVdd5YSb2rVru9yW0nSbFeXkKH/ALbJFamqqgsLDvd0MACbMHCtQ7ave1Z59rk1mNXVWti6Zq6jIctbvx/ufWqKn33R9nuB//vUHDen1b9ludeo+tbxphsvbdWufoLkvb5Ft7Pr67wJTR8bMddm+fbuaNWvmrFBq1KiR2rdvr/r166tbt27nnC9jJvx26NBBgwYN0ty5c3X48GGn0B4A+Lqw8iG6/cYmLm93a59GfhFkjLv6J7m8TXylcN3U48xpBzZq0ThOnS52fWrB3QNd32++wG/DjKkPY2rG9OrVyzkf05YtWxQXF6fJkydr9uzZTk9CSSb/GhUrVnSGmjZutH9SHIDAMHxAkuIqhJX48bHR5TRyiP9UAW5ct4IG9azv0jZjb23hBEF/8dDtreXKoqQWjSqpT5fiF834Or8NM0ZSUpJmzZrl9KqYy9KlS3XnnXc6k4NNuAkODlbz5s3P+zx79uzRhg0b1KBBA4+0GwAuVGL1KH38bA/FRJ2/pyUyPFQznu6u+on+VQl9yl87qXMJS/T/vn9T/fHWFvInPS9P1LMPlmxVbL2aMZrz3NUqV87OWBBUZMokBhgTai655BJnKGn9+tNPwPjb3/7W6YUxc2lMj8xPP/2kp59+2pk0bObbmPs8wbY5M6Hvvc6cGcAH/Zi6TyOf+FZfLy++PMRlF1XTM2M7qG2z0i1w8HU5ufl64OllmjIjtdjTOlSrHKExt7bQ/Tc3t662Skl98MVmPTRxhVK3HjzjvpCQIN14ZV1NerCjqlY+XkjWRgEZZl5++WXdcccdzlJrs0T7VJMmTdIbb7zhhJicnBzVqlXLKbz30EMPnbVmTVkgzABwp7Ub9+v1mT9pW/oRpzJwYrUo3dy7oVo1OV5Hy98dOnJMb87aqKU/7nVCTYWYcup5WaL6dquj8uX8Z2jpbMxH/bylaZr+xWbnbNlmOC2pfgXd1rexXxRJDMgwY6r4mtMV/OMf/3BCii8izAAAUDJ2Do5doNWrV5d48i8AAPBtfltnpiTnbQIAAPYLyJ4ZAADgPwgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGC1gDxrtg2cX0turrebUXJhYQoKCvJ2KwAAAYgwAwAArMYwEwAAsBphBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYjzAAAAKsRZgAAgNUIMwAAwGqEGQAAYDXCDAAAkM3+H9OhUYl4KdcgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 705.552x367.889 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autoresearch_quantum.codes.four_two_two import (\n",
    "    build_preparation_circuit, build_encoder, apply_magic_seed,\n",
    "    encoded_magic_statevector, STABILIZERS, MEASUREMENT_OPERATORS, DATA_QUBITS,\n",
    ")\n",
    "\n",
    "prep = build_preparation_circuit(seed_style=\"h_p\", encoder_style=\"cx_chain\")\n",
    "print(f\"Circuit qubits: {prep.num_qubits}\")\n",
    "print(f\"Circuit depth:  {prep.depth()}\")\n",
    "prep.draw(output=\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** The first gate (H + P on qubit 0) prepares the raw T-state. The remaining CNOT gates \"spread\" it across all 4 qubits so that single errors become detectable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Stabilizer Check: Verifying the Codeword\n",
    "\n",
    "The [[4,2,2]] code has two stabilizers: $XXXX$ and $ZZZZ$. A valid codeword must be a $+1$ eigenstate of both.\n",
    "\n",
    "$$\\langle XXXX \\rangle = +1 \\quad \\text{and} \\quad \\langle ZZZZ \\rangle = +1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<z_stabilizer> = +1.000000\n",
      "<x_stabilizer> = +1.000000\n",
      "\n",
      "Both are +1 -- the state is a valid codeword!\n"
     ]
    }
   ],
   "source": [
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "encoded_state = encoded_magic_statevector()\n",
    "\n",
    "for name, op in STABILIZERS.items():\n",
    "    expectation = encoded_state.expectation_value(op)\n",
    "    print(f\"<{name}> = {expectation.real:+.6f}\")\n",
    "\n",
    "print(\"\\nBoth are +1 -- the state is a valid codeword!\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "quiz(tracker, \"p2_q2_stabilizer\",\n",
    "    question=\"Both stabilizer expectations are +1. What does this confirm?\",\n",
    "    options=[\n",
    "        \"The state has high energy\",\n",
    "        \"The state is in the [[4,2,2]] codespace \\u2014 no errors detected\",\n",
    "        \"All qubits are in |0\\u27E9\",\n",
    "    ],\n",
    "    correct=1, section=\"Pass 2: Concepts\", bloom=\"understand\",\n",
    "    explanation=\"Stabilizer eigenvalue +1 is the codespace condition. Any single-qubit error would flip at least one to \\u22121.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** Stabilizer eigenvalues act as error flags. If an error flips one from +1 to -1, we *detect* it (though with distance 2, we cannot correct it -- we can only discard the shot)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 What a \"Shot\" Looks Like\n",
    "\n",
    "When we run the circuit on a (simulated) quantum computer, each execution is called a \"shot\". The result is a bitstring that encodes both **syndrome bits** (stabilizer measurement outcomes) and **data bits** (the actual qubit readout)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registers: ['syndrome', 'readout']\n",
      "Syndrome labels: ['z_stabilizer', 'x_stabilizer']\n",
      "\n",
      "First 8 raw shots (syndrome + readout):\n",
      "  Shot 0: syndrome=1010, readout=00\n",
      "  Shot 1: syndrome=0000, readout=00\n",
      "  Shot 2: syndrome=1111, readout=00\n",
      "  Shot 3: syndrome=0101, readout=00\n",
      "  Shot 4: syndrome=1010, readout=00\n",
      "  Shot 5: syndrome=0000, readout=00\n",
      "  Shot 6: syndrome=1111, readout=00\n",
      "  Shot 7: syndrome=1111, readout=00\n"
     ]
    }
   ],
   "source": [
    "from autoresearch_quantum.experiments.encoded_magic_state import build_circuit_bundle\n",
    "from autoresearch_quantum.models import ExperimentSpec\n",
    "from qiskit_aer import AerSimulator\n",
    "\n",
    "spec_demo = ExperimentSpec(\n",
    "    rung=1,\n",
    "    seed_style=\"h_p\",\n",
    "    encoder_style=\"cx_chain\",\n",
    "    verification=\"both\",\n",
    "    postselection=\"all_measured\",\n",
    "    ancilla_strategy=\"dedicated_pair\",\n",
    "    shots=256,\n",
    "    repeats=1,\n",
    ")\n",
    "\n",
    "bundle = build_circuit_bundle(spec_demo)\n",
    "acceptance_circuit = bundle.acceptance\n",
    "\n",
    "print(f\"Registers: {[creg.name for creg in acceptance_circuit.cregs]}\")\n",
    "print(f\"Syndrome labels: {acceptance_circuit.metadata.get('syndrome_labels', [])}\")\n",
    "print()\n",
    "\n",
    "# Run on ideal simulator with memory=True to get individual shots\n",
    "sim = AerSimulator()\n",
    "result = sim.run(acceptance_circuit, shots=16, memory=True).result()\n",
    "memory = result.get_memory(acceptance_circuit)\n",
    "\n",
    "print(\"First 8 raw shots (syndrome + readout):\")\n",
    "for i, shot in enumerate(memory[:8]):\n",
    "    parts = shot.split(\" \")\n",
    "    print(f\"  Shot {i}: syndrome={parts[0]}, readout={parts[1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The syndrome register has 2 bits: one for $ZZZZ$ and one for $XXXX$. On an ideal simulator, syndrome is always `00` (no errors detected). The readout register has 4 bits -- the measured data qubits.\n",
    "\n",
    "> **Key Insight:** Each shot gives us a syndrome (error flag) and a data readout. We use the syndrome to decide whether to keep or discard the shot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Postselection: Keeping Only Good Shots\n",
    "\n",
    "**Postselection** means discarding shots where the syndrome indicates an error was detected. If syndrome = `00`, both stabilizers measured $+1$ and we keep the shot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total shots:    256\n",
      "Accepted shots: 256\n",
      "Acceptance rate: 1.0000\n",
      "\n",
      "Syndrome distribution: {'00': 256}\n",
      "\n",
      "(On an ideal simulator, acceptance rate should be 1.0 -- no errors!)\n"
     ]
    }
   ],
   "source": [
    "from autoresearch_quantum.execution.analysis import (\n",
    "    local_memory_records, summarize_context, logical_magic_witness, stability_score\n",
    ")\n",
    "\n",
    "# Run with more shots for statistics\n",
    "result_256 = sim.run(acceptance_circuit, shots=256, memory=True).result()\n",
    "memory_256 = result_256.get_memory(acceptance_circuit)\n",
    "\n",
    "creg_names = [creg.name for creg in acceptance_circuit.cregs]\n",
    "records = local_memory_records(memory_256, creg_names)\n",
    "\n",
    "syndrome_labels = list(acceptance_circuit.metadata.get(\"syndrome_labels\", []))\n",
    "summary = summarize_context(\n",
    "    records,\n",
    "    syndrome_labels=syndrome_labels,\n",
    "    postselection=\"all_measured\",\n",
    "    operator=None,\n",
    ")\n",
    "\n",
    "print(f\"Total shots:    {summary['total_shots']}\")\n",
    "print(f\"Accepted shots: {summary['accepted_shots']}\")\n",
    "print(f\"Acceptance rate: {summary['acceptance_rate']:.4f}\")\n",
    "print(f\"\\nSyndrome distribution: {summary['syndrome_counts']}\")\n",
    "print(\"\\n(On an ideal simulator, acceptance rate should be 1.0 -- no errors!)\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "quiz(tracker, \"p2_q3_postselection\",\n",
    "    question=\"Postselection improves quality by discarding error-flagged shots. What is the cost?\",\n",
    "    options=[\n",
    "        \"It makes the circuit deeper\",\n",
    "        \"You lose shots \\u2014 fewer usable data points\",\n",
    "        \"It introduces new types of errors\",\n",
    "    ],\n",
    "    correct=1, section=\"Pass 2: Concepts\", bloom=\"understand\",\n",
    "    explanation=\"Postselection trades quantity for quality. Fewer usable shots means worse statistics or more total shots needed.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 Adding Noise: What Happens on Real Hardware\n",
    "\n",
    "Real quantum processors have gate errors, readout errors, and decoherence. We simulate this using a noise model extracted from IBM's `fake_brisbane` backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noisy acceptance rate: 0.7656\n",
      "Ideal acceptance rate: 1.0000\n",
      "\n",
      "Noise dropped acceptance by 0.2344\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autoresearch_quantum.execution.backends import resolve_backend\n",
    "from autoresearch_quantum.execution.transpile import transpile_circuits\n",
    "from qiskit_aer.noise import NoiseModel\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "backend = resolve_backend(\"fake_brisbane\")\n",
    "noise_model = NoiseModel.from_backend(backend)\n",
    "\n",
    "noisy_sim = AerSimulator(\n",
    "    noise_model=noise_model,\n",
    "    basis_gates=noise_model.basis_gates,\n",
    ")\n",
    "\n",
    "# Transpile for the backend\n",
    "transpiled_acceptance = transpile_circuits([acceptance_circuit], spec_demo, backend)[0]\n",
    "\n",
    "result_noisy = noisy_sim.run(transpiled_acceptance, shots=512, memory=True).result()\n",
    "memory_noisy = result_noisy.get_memory(transpiled_acceptance)\n",
    "\n",
    "records_noisy = local_memory_records(memory_noisy, [creg.name for creg in transpiled_acceptance.cregs])\n",
    "summary_noisy = summarize_context(\n",
    "    records_noisy,\n",
    "    syndrome_labels=syndrome_labels,\n",
    "    postselection=\"all_measured\",\n",
    "    operator=None,\n",
    ")\n",
    "\n",
    "print(f\"Noisy acceptance rate: {summary_noisy['acceptance_rate']:.4f}\")\n",
    "print(f\"Ideal acceptance rate: {summary['acceptance_rate']:.4f}\")\n",
    "print(f\"\\nNoise dropped acceptance by {summary['acceptance_rate'] - summary_noisy['acceptance_rate']:.4f}\")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 4))\n",
    "ax1.bar(summary[\"syndrome_counts\"].keys(), summary[\"syndrome_counts\"].values(), color=\"#2ecc71\")\n",
    "ax1.set_title(\"Ideal: Syndrome Distribution\")\n",
    "ax1.set_xlabel(\"Syndrome\")\n",
    "ax1.set_ylabel(\"Counts\")\n",
    "\n",
    "ax2.bar(summary_noisy[\"syndrome_counts\"].keys(), summary_noisy[\"syndrome_counts\"].values(), color=\"#e74c3c\")\n",
    "ax2.set_title(\"Noisy: Syndrome Distribution\")\n",
    "ax2.set_xlabel(\"Syndrome\")\n",
    "ax2.set_ylabel(\"Counts\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** Noise causes some shots to fail the stabilizer check (syndrome != `00`). The error-detecting code is working -- it flags corrupted shots so we can discard them. But this comes at a cost: lower acceptance rate means we need more shots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7 The Witness Formula: Measuring Magic\n",
    "\n",
    "How do we know our encoded state is actually the *right* magic state and not just any codeword? We measure three logical operators and combine them into a **witness** value.\n",
    "\n",
    "The logical operators for the [[4,2,2]] code are:\n",
    "- $X_L = X_0 X_2$ (logical X on the magic qubit)\n",
    "- $Y_L = Y_0 Z_1 X_2$ (logical Y on the magic qubit)\n",
    "- $Z_{\\text{spectator}} = Z_1 Z_2$ (logical Z on the spectator qubit)\n",
    "\n",
    "The witness formula is:\n",
    "\n",
    "$$\\text{witness} = \\underbrace{\\frac{1 + \\frac{\\langle X_L \\rangle + \\langle Y_L \\rangle}{\\sqrt{2}}}{2}}_{\\text{magic quality}} \\times \\underbrace{\\frac{1 + \\langle Z_{\\text{spectator}} \\rangle}{2}}_{\\text{spectator alignment}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<     logical_x> = +0.5071  (acceptance: 0.5469)\n",
      "<     logical_y> = +0.3569  (acceptance: 0.6621)\n",
      "<   spectator_z> = +0.0000  (acceptance: 0.5312)\n",
      "\n",
      "--- Witness Computation ---\n",
      "  (X_L + Y_L) / sqrt(2) = (0.5071 + 0.3569) / 1.4142 = 0.6110\n",
      "  Magic quality   = (1 + 0.6110) / 2 = 0.8055\n",
      "  Spectator align = (1 + 0.0000) / 2 = 0.5000\n",
      "  Witness = 0.8055 x 0.5000 = 0.4027\n",
      "  Library check:  0.4027\n"
     ]
    }
   ],
   "source": [
    "# Measure each logical operator on the noisy simulator\n",
    "witness_circuits = bundle.witness_circuits\n",
    "transpiled_witnesses = {}\n",
    "for name, circ in witness_circuits.items():\n",
    "    transpiled_witnesses[name] = transpile_circuits([circ], spec_demo, backend)[0]\n",
    "\n",
    "expectations = {}\n",
    "for name, circ in transpiled_witnesses.items():\n",
    "    result_w = noisy_sim.run(circ, shots=512, memory=True).result()\n",
    "    mem_w = result_w.get_memory(circ)\n",
    "    recs_w = local_memory_records(mem_w, [creg.name for creg in circ.cregs])\n",
    "    summary_w = summarize_context(\n",
    "        recs_w,\n",
    "        syndrome_labels=syndrome_labels,\n",
    "        postselection=\"all_measured\",\n",
    "        operator=MEASUREMENT_OPERATORS.get(name),\n",
    "    )\n",
    "    expectations[name] = summary_w[\"expectation\"]\n",
    "    print(f\"<{name:>14}> = {summary_w['expectation']:+.4f}  (acceptance: {summary_w['acceptance_rate']:.4f})\")\n",
    "\n",
    "# Build up the witness step by step\n",
    "lx = expectations[\"logical_x\"]\n",
    "ly = expectations[\"logical_y\"]\n",
    "sz = expectations[\"spectator_z\"]\n",
    "\n",
    "magic_quality = (1.0 + (lx + ly) / sqrt(2)) / 2.0\n",
    "spectator_alignment = (1.0 + sz) / 2.0\n",
    "witness_value = magic_quality * spectator_alignment\n",
    "\n",
    "# Also compute via the library function\n",
    "witness_lib = logical_magic_witness(lx, ly, sz)\n",
    "\n",
    "print(f\"\\n--- Witness Computation ---\")\n",
    "print(f\"  (X_L + Y_L) / sqrt(2) = ({lx:.4f} + {ly:.4f}) / {sqrt(2):.4f} = {(lx + ly)/sqrt(2):.4f}\")\n",
    "print(f\"  Magic quality   = (1 + {(lx+ly)/sqrt(2):.4f}) / 2 = {magic_quality:.4f}\")\n",
    "print(f\"  Spectator align = (1 + {sz:.4f}) / 2 = {spectator_alignment:.4f}\")\n",
    "print(f\"  Witness = {magic_quality:.4f} x {spectator_alignment:.4f} = {witness_value:.4f}\")\n",
    "print(f\"  Library check:  {witness_lib:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Connection to Pass 1:** This witness value is the `logical_magic_witness` field in the evaluation metrics. It is the core measure of **quality** -- the thing the ratchet was trying to maximize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8 Cost: Why Circuit Complexity Matters\n",
    "\n",
    "Longer circuits accumulate more errors. The cost function penalizes experiments with many two-qubit gates, deep circuits, and large shot counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     logical_x: depth= 83, two-qubit gates= 30, size=192\n",
      "     logical_y: depth= 93, two-qubit gates= 30, size=184\n",
      "   spectator_z: depth=108, two-qubit gates= 32, size=209\n",
      "\n",
      "Cost formula from rung1 config:\n",
      "  cost = 1.0 + 0.08*two_q + 0.01*depth + 0.0002*shots + 0.015*runtime + 0.3*queue\n"
     ]
    }
   ],
   "source": [
    "from autoresearch_quantum.execution.transpile import count_two_qubit_gates, circuit_metadata\n",
    "\n",
    "for name, circ in transpiled_witnesses.items():\n",
    "    tqg = count_two_qubit_gates(circ)\n",
    "    d = circ.depth()\n",
    "    meta = circuit_metadata(circ, spec_demo)\n",
    "    print(f\"{name:>14}: depth={d:3d}, two-qubit gates={tqg:3d}, size={meta['size']}\")\n",
    "\n",
    "# Show the cost formula\n",
    "print(f\"\\nCost formula from rung1 config:\")\n",
    "cw = rung1_config.score.cost_weights\n",
    "print(f\"  cost = {rung1_config.score.base_cost} + {cw.two_qubit_count}*two_q + {cw.depth}*depth + {cw.shot_count}*shots + {cw.runtime_estimate}*runtime + {cw.queue_cost_proxy}*queue\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "predict_choice(tracker, \"p2_q4_cost_quality\",\n",
    "    question=\"More complex circuits might give better quality but higher cost. What does the score formula do with this tension?\",\n",
    "    options=[\n",
    "        \"Ignores cost entirely \\u2014 only quality matters\",\n",
    "        \"Divides quality by cost, so you need quality to outweigh the cost\",\n",
    "        \"Picks the cheapest circuit regardless of quality\",\n",
    "    ],\n",
    "    correct=1, section=\"Pass 2: Scoring\", bloom=\"apply\",\n",
    "    explanation=\"score = quality \\u00d7 acceptance / cost. A circuit that is 2x better but 3x more expensive scores worse.\")\n",
    "\n",
    "checkpoint_summary(tracker, \"Pass 2: Scoring\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** There is a tension between quality and cost. More complex circuits might give better error detection, but they also introduce more errors. The scoring function balances this tradeoff."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9 The Score: Putting It All Together\n",
    "\n",
    "The final score combines **quality** (witness, fidelity, stability, etc.), **acceptance rate** (fraction of shots that pass postselection), and **cost** (circuit complexity):\n",
    "\n",
    "$$\\text{score} = \\frac{\\text{quality} \\times \\text{acceptance\\_rate}}{\\text{cost}}$$\n",
    "\n",
    "where quality is a weighted sum of individual metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quality:         0.730531\n",
      "Acceptance rate: 0.765625\n",
      "Cost:            9.542400\n",
      "Score:           0.058613\n",
      "\n",
      "Score = 0.7305 * 0.7656 / 9.5424 = 0.058613\n"
     ]
    }
   ],
   "source": [
    "from autoresearch_quantum.scoring.score import score_metrics, weighted_acceptance_cost\n",
    "from autoresearch_quantum.models import EvaluationMetrics\n",
    "\n",
    "# Build metrics from our manual measurements\n",
    "manual_metrics = EvaluationMetrics(\n",
    "    ideal_encoded_fidelity=1.0,  # perfect on ideal sim\n",
    "    noisy_encoded_fidelity=0.85,  # placeholder\n",
    "    logical_magic_witness=witness_lib,\n",
    "    acceptance_rate=summary_noisy[\"acceptance_rate\"],\n",
    "    codespace_rate=summary_noisy[\"acceptance_rate\"],\n",
    "    spectator_logical_z=sz,\n",
    "    logical_x=lx,\n",
    "    logical_y=ly,\n",
    "    stability_score=1.0,\n",
    "    two_qubit_count=sum(count_two_qubit_gates(c) for c in transpiled_witnesses.values()),\n",
    "    depth=max(c.depth() for c in transpiled_witnesses.values()),\n",
    "    shot_count=512,\n",
    ")\n",
    "\n",
    "score, quality, cost = score_metrics(manual_metrics, \"cheap\", rung1_config.score)\n",
    "\n",
    "print(f\"Quality:         {quality:.6f}\")\n",
    "print(f\"Acceptance rate: {summary_noisy['acceptance_rate']:.6f}\")\n",
    "print(f\"Cost:            {cost:.6f}\")\n",
    "print(f\"Score:           {score:.6f}\")\n",
    "print(f\"\\nScore = {quality:.4f} * {summary_noisy['acceptance_rate']:.4f} / {cost:.4f} = {score:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Connection to Pass 1:** This is exactly the score number you saw in the bar chart. Every bar in that chart was computed using this formula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10 What Are Challengers?\n",
    "\n",
    "The ratchet starts with an **incumbent** (the current best experiment settings). It then generates **challengers** by mutating one or more parameters. Each challenger is evaluated and compared to the incumbent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incumbent settings:\n",
      "  seed_style:        h_p\n",
      "  encoder_style:     cx_chain\n",
      "  verification:      both\n",
      "  postselection:     all_measured\n",
      "  ancilla_strategy:  dedicated_pair\n",
      "  optimization_level:2\n",
      "\n",
      "Generated 8 challengers:\n",
      "  #  Mutation                                          \n",
      "-------------------------------------------------------\n",
      "  1  seed_style: h_p -> ry_rz\n",
      "  2  seed_style: h_p -> u_magic\n",
      "  3  encoder_style: cx_chain -> cz_compiled\n",
      "  4  verification: both -> z_only\n",
      "  5  verification: both -> x_only\n",
      "  6  postselection: all_measured -> z_only\n",
      "  7  postselection: all_measured -> none\n",
      "  8  ancilla_strategy: dedicated_pair -> reused_single\n"
     ]
    }
   ],
   "source": [
    "from autoresearch_quantum.search.challengers import generate_neighbor_challengers, mutation_summary\n",
    "\n",
    "incumbent_spec = rung1_config.bootstrap_incumbent\n",
    "print(\"Incumbent settings:\")\n",
    "print(f\"  seed_style:        {incumbent_spec.seed_style}\")\n",
    "print(f\"  encoder_style:     {incumbent_spec.encoder_style}\")\n",
    "print(f\"  verification:      {incumbent_spec.verification}\")\n",
    "print(f\"  postselection:     {incumbent_spec.postselection}\")\n",
    "print(f\"  ancilla_strategy:  {incumbent_spec.ancilla_strategy}\")\n",
    "print(f\"  optimization_level:{incumbent_spec.optimization_level}\")\n",
    "\n",
    "# Generate challengers\n",
    "challengers = generate_neighbor_challengers(incumbent_spec, rung1_config.search_space)\n",
    "\n",
    "print(f\"\\nGenerated {len(challengers)} challengers:\")\n",
    "print(f\"{'#':>3}  {'Mutation':50s}\")\n",
    "print(\"-\" * 55)\n",
    "for i, ch in enumerate(challengers):\n",
    "    print(f\"{i+1:3d}  {ch.mutation_note}\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "quiz(tracker, \"p2_q5_neighbors\",\n",
    "    question=\"Each NeighborWalk challenger differs from the incumbent in how many parameters?\",\n",
    "    options=[\"0\", \"1\", \"2\", \"All of them\"],\n",
    "    correct=1, section=\"Pass 2: Ratchet\", bloom=\"apply\",\n",
    "    explanation=\"NeighborWalk changes exactly one parameter at a time. This is systematic but cannot find parameter interactions.\")\n",
    "\n",
    "checkpoint_summary(tracker, \"Pass 2: Ratchet\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each challenger differs from the incumbent in exactly one parameter (this is the `NeighborWalk` strategy). The ratchet also uses `RandomCombo` to mutate multiple parameters at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting                                            Score    Quality     Accept\n",
      "--------------------------------------------------------------------------------\n",
      "INCUMBENT                                       0.035552   0.851271     1.0000\n",
      "seed_style: h_p -> ry_rz                        0.035362   0.854142     1.0000\n",
      "seed_style: h_p -> u_magic                      0.036333   0.853074     1.0000\n",
      "encoder_style: cx_chain -> cz_compiled          0.035371   0.841448     1.0000\n",
      "verification: both -> z_only                    0.069126   0.855724     1.0000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate a few challengers and compare\n",
    "from autoresearch_quantum.execution.local import LocalCheapExecutor\n",
    "\n",
    "executor = LocalCheapExecutor()\n",
    "\n",
    "print(f\"{'Setting':45s} {'Score':>10s} {'Quality':>10s} {'Accept':>10s}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Evaluate incumbent\n",
    "inc_result = executor.evaluate(incumbent_spec, rung1_config)\n",
    "print(f\"{'INCUMBENT':45s} {inc_result.score:10.6f} {inc_result.quality_estimate:10.6f} {inc_result.metrics.acceptance_rate:10.4f}\")\n",
    "\n",
    "# Evaluate first 4 challengers\n",
    "for ch in challengers[:4]:\n",
    "    ch_result = executor.evaluate(ch.spec, rung1_config)\n",
    "    label = ch.mutation_note[:45]\n",
    "    print(f\"{label:45s} {ch_result.score:10.6f} {ch_result.quality_estimate:10.6f} {ch_result.metrics.acceptance_rate:10.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Connection to Pass 1:** In Pass 1, the ratchet did this automatically -- generated challengers, evaluated all of them, and picked the best."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.11 Promotion and Winner Selection\n",
    "\n",
    "Not every challenger becomes the new incumbent. The ratchet uses a **cheap margin** to filter: a challenger must beat the incumbent's score by at least this threshold.\n",
    "\n",
    "$$\\text{challenger\\_score} > \\text{incumbent\\_score} + \\text{cheap\\_margin}$$\n",
    "\n",
    "Only the top-K challengers that clear this bar get \"promoted\". The best one among promoted challengers (and the incumbent) becomes the new incumbent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cheap margin:   0.002\n",
      "Promote top-K:  2\n",
      "\n",
      "Incumbent score: 0.035552\n",
      "Threshold:       0.037552\n",
      "\n",
      "For a challenger to be promoted, it must score above 0.037552\n"
     ]
    }
   ],
   "source": [
    "cheap_margin = rung1_config.tier_policy.cheap_margin\n",
    "promote_top_k = rung1_config.tier_policy.promote_top_k\n",
    "\n",
    "print(f\"Cheap margin:   {cheap_margin}\")\n",
    "print(f\"Promote top-K:  {promote_top_k}\")\n",
    "print(f\"\\nIncumbent score: {inc_result.score:.6f}\")\n",
    "print(f\"Threshold:       {inc_result.score + cheap_margin:.6f}\")\n",
    "print(f\"\\nFor a challenger to be promoted, it must score above {inc_result.score + cheap_margin:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.12 Re-Running the Ratchet Step (With Understanding)\n",
    "\n",
    "Let's run the same ratchet step from Pass 1, but now we understand every decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incumbent ID:    r1-incumbent-4343a2eac0\n",
      "Incumbent score: 0.035164\n",
      "  witness:  0.5105\n",
      "  accept:   1.0000\n",
      "  cost:     24.2192\n",
      "\n",
      "--- Running challengers ---\n",
      "\n",
      "                      ID       Score      Margin   Promoted?  Mutation\n",
      "-----------------------------------------------------------------------------------------------\n",
      "r1-challenger-49ab8874a8    0.068216   +0.033052         YES  combo: postselection: all_measured -> z_\n",
      "r1-challenger-b8c950b594    0.053963   +0.018798         YES  combo: encoder_style: cx_chain -> cz_com\n",
      "r1-challenger-a39c019eb7    0.048566   +0.013402         YES  combo: verification: both -> z_only, opt\n",
      "r1-challenger-6a036b7f5f    0.036716   +0.001551          no  neighbor: seed_style: h_p -> u_magic\n",
      "r1-challenger-045a4145b4    0.036413   +0.001249          no  combo: postselection: all_measured -> z_\n",
      "r1-challenger-49ccceae73    0.035814   +0.000650          no  combo: encoder_style: cx_chain -> cz_com\n",
      "r1-challenger-2bc9c87de9    0.035086   -0.000078          no  neighbor: seed_style: h_p -> ry_rz\n",
      "r1-challenger-3b16c89d93    0.034822   -0.000342          no  neighbor: encoder_style: cx_chain -> cz_\n"
     ]
    }
   ],
   "source": [
    "# Fresh store\n",
    "store_p2 = ResearchStore(tempfile.mkdtemp())\n",
    "harness_p2 = AutoresearchHarness(store_p2)\n",
    "\n",
    "# Ensure incumbent\n",
    "incumbent_record = harness_p2.ensure_incumbent(rung1_config)\n",
    "print(f\"Incumbent ID:    {incumbent_record.experiment_id}\")\n",
    "print(f\"Incumbent score: {incumbent_record.cheap_result.score:.6f}\")\n",
    "print(f\"  witness:  {incumbent_record.cheap_result.metrics.logical_magic_witness:.4f}\")\n",
    "print(f\"  accept:   {incumbent_record.cheap_result.metrics.acceptance_rate:.4f}\")\n",
    "print(f\"  cost:     {incumbent_record.cheap_result.metrics.total_cost:.4f}\")\n",
    "\n",
    "# Run challenger set\n",
    "print(\"\\n--- Running challengers ---\")\n",
    "challenger_records = harness_p2.run_challenger_set(rung1_config)\n",
    "\n",
    "print(f\"\\n{'ID':>24s}  {'Score':>10s}  {'Margin':>10s}  {'Promoted?':>10s}  Mutation\")\n",
    "print(\"-\" * 95)\n",
    "for rec in sorted(challenger_records, key=lambda r: r.cheap_result.score, reverse=True):\n",
    "    margin = rec.cheap_result.score - incumbent_record.cheap_result.score\n",
    "    promoted = \"YES\" if margin > cheap_margin else \"no\"\n",
    "    print(f\"{rec.experiment_id[:24]:>24s}  {rec.cheap_result.score:10.6f}  {margin:+10.6f}  {promoted:>10s}  {rec.mutation_note[:40]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Connection to Pass 1:** You are now seeing the same optimization process from Pass 1, but with full visibility into the scores, margins, and promotion decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.13 The Lesson Revisited\n",
    "\n",
    "Let's re-read the lesson from our Pass 1 rung. Now every term should make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Markdown' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m display(Markdown(lesson.narrative))\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m print(\u001b[33m\"\\n--- Now you know what each term means ---\"\u001b[39m)\n\u001b[32m      4\u001b[39m print(\u001b[33m\"- 'verification=both' means measuring both XXXX and ZZZZ stabilizers\"\u001b[39m)\n",
      "\u001b[31mNameError\u001b[39m: name 'Markdown' is not defined"
     ]
    }
   ],
   "source": [
    "display(Markdown(lesson.narrative))\n",
    "\n",
    "print(\"\\n--- Now you know what each term means ---\")\n",
    "print(\"- 'verification=both' means measuring both XXXX and ZZZZ stabilizers\")\n",
    "print(\"- 'logical witness' is the magic_quality x spectator_alignment formula\")\n",
    "print(\"- 'acceptance rate' is the fraction of shots where syndrome=00\")\n",
    "print(\"- 'score' = quality * acceptance / cost\")\n",
    "print(\"- 'mean score improved by +X' means a challenger beat the incumbent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Pass 3: Making It Your Own\n",
    "\n",
    "You have seen the system run (Pass 1) and understood every component (Pass 2). Now you will **drive** -- modify parameters, compare scoring functions, and run multi-step optimizations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Change the Search Space\n",
    "\n",
    "What if we restrict the search to only a few dimensions? Or expand it? Let's modify the rung config and see how the challengers change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import replace\n",
    "from autoresearch_quantum.models import SearchSpaceConfig\n",
    "\n",
    "# Create a search space that only explores verification and postselection\n",
    "narrow_space = SearchSpaceConfig(\n",
    "    dimensions={\n",
    "        \"verification\": [\"both\", \"z_only\", \"x_only\"],\n",
    "        \"postselection\": [\"all_measured\", \"z_only\", \"none\"],\n",
    "    },\n",
    "    max_challengers_per_step=6,\n",
    ")\n",
    "\n",
    "narrow_config = replace(rung1_config, search_space=narrow_space, step_budget=2, patience=2)\n",
    "\n",
    "# Run a ratchet step with this narrower search\n",
    "store_narrow = ResearchStore(tempfile.mkdtemp())\n",
    "harness_narrow = AutoresearchHarness(store_narrow)\n",
    "step_narrow = harness_narrow.run_ratchet_step(narrow_config)\n",
    "\n",
    "print(f\"Challengers tested: {len(step_narrow.challengers_tested)}\")\n",
    "print(f\"Winner: {step_narrow.winner_id}\")\n",
    "print(f\"Margin: {step_narrow.winning_margin:+.6f}\")\n",
    "print(f\"Lesson: {step_narrow.distilled_lesson}\")\n",
    "\n",
    "# Show which experiments were tried\n",
    "exps_narrow = store_narrow.list_experiments(1)\n",
    "for e in exps_narrow:\n",
    "    role = e[\"role\"]\n",
    "    v = e[\"spec\"][\"verification\"]\n",
    "    p = e[\"spec\"][\"postselection\"]\n",
    "    s = e[\"final_score\"]\n",
    "    print(f\"  {role:12s}  verification={v:12s}  postselection={p:14s}  score={s:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Compare Scoring Functions\n",
    "\n",
    "The system supports multiple scoring functions. `weighted_acceptance_cost` balances quality and cost traditionally. `factory_throughput` optimizes for accepted magic states per unit cost, penalizing circuit complexity more heavily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autoresearch_quantum.scoring.score import weighted_acceptance_cost, factory_throughput_score\n",
    "from autoresearch_quantum.models import ScoreConfig\n",
    "\n",
    "# Evaluate the same set of specs with both scoring functions\n",
    "test_specs = [\n",
    "    rung1_config.bootstrap_incumbent,\n",
    "    rung1_config.bootstrap_incumbent.with_updates(verification=\"z_only\"),\n",
    "    rung1_config.bootstrap_incumbent.with_updates(verification=\"x_only\"),\n",
    "    rung1_config.bootstrap_incumbent.with_updates(postselection=\"none\"),\n",
    "    rung1_config.bootstrap_incumbent.with_updates(encoder_style=\"cz_compiled\"),\n",
    "]\n",
    "\n",
    "labels = [\"baseline\", \"z_only_verif\", \"x_only_verif\", \"no_postsel\", \"cz_compiled\"]\n",
    "\n",
    "wac_scores = []\n",
    "ft_scores = []\n",
    "\n",
    "factory_score_config = replace(rung1_config.score, name=\"factory_throughput\")\n",
    "\n",
    "for spec_test in test_specs:\n",
    "    result_test = executor.evaluate(spec_test, rung1_config)\n",
    "    metrics = result_test.metrics\n",
    "    \n",
    "    s_wac, _, _ = weighted_acceptance_cost(metrics, \"cheap\", rung1_config.score)\n",
    "    # Need fresh metrics for factory scoring since it modifies metrics.extra\n",
    "    result_test2 = executor.evaluate(spec_test, rung1_config)\n",
    "    s_ft, _, _ = factory_throughput_score(result_test2.metrics, \"cheap\", factory_score_config)\n",
    "    \n",
    "    wac_scores.append(s_wac)\n",
    "    ft_scores.append(s_ft)\n",
    "\n",
    "x = np.arange(len(labels))\n",
    "width = 0.35\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "ax1.bar(x, wac_scores, width, color=\"#3498db\", label=\"weighted_acceptance_cost\")\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(labels, rotation=30, ha=\"right\")\n",
    "ax1.set_ylabel(\"Score\")\n",
    "ax1.set_title(\"weighted_acceptance_cost\")\n",
    "\n",
    "ax2.bar(x, ft_scores, width, color=\"#e67e22\", label=\"factory_throughput\")\n",
    "ax2.set_xticks(x)\n",
    "ax2.set_xticklabels(labels, rotation=30, ha=\"right\")\n",
    "ax2.set_ylabel(\"Score\")\n",
    "ax2.set_title(\"factory_throughput\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Show ranking differences\n",
    "wac_rank = np.argsort(wac_scores)[::-1]\n",
    "ft_rank = np.argsort(ft_scores)[::-1]\n",
    "print(\"Ranking by weighted_acceptance_cost:\", [labels[i] for i in wac_rank])\n",
    "print(\"Ranking by factory_throughput:      \", [labels[i] for i in ft_rank])"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "reflect(tracker, \"p3_q1_scoring_choice\",\n",
    "    question=\"You see that different scoring functions rank experiments differently. When would you choose factory throughput over WAC?\",\n",
    "    section=\"Pass 3: Scoring\", bloom=\"evaluate\",\n",
    "    model_answer=\"Factory throughput penalizes cost more heavily. Use it when you are producing many T-states in a pipeline and throughput matters more than per-state quality.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** Different scoring functions can re-order the rankings. The factory throughput scorer penalizes circuit complexity more heavily, which can favor simpler circuits even if their raw quality is slightly lower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Run a Full Multi-Step Rung and Visualize the Trajectory\n",
    "\n",
    "Let's run a full rung with `step_budget=3` and watch the score evolve over steps. The patience mechanism will stop early if the ratchet stalls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_multi = ResearchStore(tempfile.mkdtemp())\n",
    "harness_multi = AutoresearchHarness(store_multi)\n",
    "\n",
    "config_multi = replace(rung1_config, step_budget=3, patience=2)\n",
    "steps_multi, lesson_multi, feedback_multi = harness_multi.run_rung(config_multi)\n",
    "\n",
    "# Extract trajectory\n",
    "step_indices = [s.step_index for s in steps_multi]\n",
    "winning_margins = [s.winning_margin for s in steps_multi]\n",
    "winner_ids = [s.winner_id[:16] for s in steps_multi]\n",
    "\n",
    "# Get incumbent score at each step by loading experiment\n",
    "incumbent_scores = []\n",
    "for s in steps_multi:\n",
    "    exp_data = store_multi.load_experiment(1, s.winner_id)\n",
    "    incumbent_scores.append(exp_data[\"final_score\"])\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "ax1.plot(step_indices, incumbent_scores, \"o-\", color=\"#2ecc71\", linewidth=2, markersize=8)\n",
    "ax1.set_xlabel(\"Step\")\n",
    "ax1.set_ylabel(\"Winner Score\")\n",
    "ax1.set_title(\"Score Trajectory Over Ratchet Steps\")\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "colors_margin = [\"#2ecc71\" if m > 0 else \"#e74c3c\" for m in winning_margins]\n",
    "ax2.bar(step_indices, winning_margins, color=colors_margin)\n",
    "ax2.axhline(y=0, color=\"black\", linewidth=0.5)\n",
    "ax2.set_xlabel(\"Step\")\n",
    "ax2.set_ylabel(\"Winning Margin\")\n",
    "ax2.set_title(\"Margin Over Previous Incumbent\")\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Steps completed: {len(steps_multi)} / {config_multi.step_budget}\")\n",
    "for s in steps_multi:\n",
    "    print(f\"  Step {s.step_index}: margin={s.winning_margin:+.6f}  challengers={len(s.challengers_tested)}  promoted={len(s.promoted_challengers)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#9997; Code Challenge\n",
    "\n",
    "Write code to compute the **cumulative best score** at each step of the multi-step rung.\n",
    "That is, for each step, track the highest score seen so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Visualize the Exploration Path\n",
    "\n",
    "For each step, what parameter changed and by how much did the score shift?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect step-by-step exploration data\n",
    "ratchet_steps_data = store_multi.list_ratchet_steps(1)\n",
    "\n",
    "print(f\"{'Step':>5s}  {'Winner':>20s}  {'Margin':>10s}  {'Lesson (first 80 chars)'}\")\n",
    "print(\"-\" * 120)\n",
    "for step_data in ratchet_steps_data:\n",
    "    print(\n",
    "        f\"{step_data['step_index']:5d}  \"\n",
    "        f\"{step_data['winner_id'][:20]:>20s}  \"\n",
    "        f\"{step_data['winning_margin']:+10.6f}  \"\n",
    "        f\"{step_data['distilled_lesson'][:80]}\"\n",
    "    )\n",
    "\n",
    "# Show all experiments as a parameter heatmap\n",
    "all_exps = store_multi.list_experiments(1)\n",
    "dimensions = list(rung1_config.search_space.dimensions.keys())\n",
    "\n",
    "print(f\"\\n{'ID':>20s}  {'Score':>10s}  {'Role':>12s}\", end=\"\")\n",
    "for d in dimensions[:4]:\n",
    "    print(f\"  {d:>18s}\", end=\"\")\n",
    "print()\n",
    "print(\"-\" * 110)\n",
    "for e in sorted(all_exps, key=lambda x: x[\"final_score\"], reverse=True)[:10]:\n",
    "    print(f\"{e['experiment_id'][:20]:>20s}  {e['final_score']:10.6f}  {e['role']:>12s}\", end=\"\")\n",
    "    for d in dimensions[:4]:\n",
    "        print(f\"  {str(e['spec'].get(d, ''))[:18]:>18s}\", end=\"\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Search Strategies Head-to-Head\n",
    "\n",
    "The system uses three search strategies:\n",
    "- **NeighborWalk**: Change one parameter at a time (systematic)\n",
    "- **RandomCombo**: Change 1-3 parameters at once (exploratory)\n",
    "- **LessonGuided**: Use rules from previous rungs to bias the search (adaptive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autoresearch_quantum.search.strategies import NeighborWalk, RandomCombo, LessonGuided\n",
    "\n",
    "incumbent_for_test = rung1_config.bootstrap_incumbent\n",
    "space_for_test = rung1_config.search_space\n",
    "\n",
    "nw = NeighborWalk()\n",
    "rc = RandomCombo(num_candidates=8)\n",
    "\n",
    "nw_challengers = nw.generate(incumbent_for_test, space_for_test, set())\n",
    "rc_challengers = rc.generate(incumbent_for_test, space_for_test, set())\n",
    "\n",
    "print(f\"NeighborWalk generated {len(nw_challengers)} challengers\")\n",
    "print(f\"RandomCombo generated  {len(rc_challengers)} challengers\")\n",
    "\n",
    "# Count mutations per dimension for each strategy\n",
    "def count_dimensions(challengers):\n",
    "    dim_counts = {}\n",
    "    for ch in challengers:\n",
    "        for dim in dimensions:\n",
    "            if dim in ch.mutation_note:\n",
    "                dim_counts[dim] = dim_counts.get(dim, 0) + 1\n",
    "    return dim_counts\n",
    "\n",
    "nw_dims = count_dimensions(nw_challengers)\n",
    "rc_dims = count_dimensions(rc_challengers)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "if nw_dims:\n",
    "    ax1.barh(list(nw_dims.keys()), list(nw_dims.values()), color=\"#3498db\")\n",
    "    ax1.set_title(\"NeighborWalk: Mutations per Dimension\")\n",
    "    ax1.set_xlabel(\"Count\")\n",
    "\n",
    "if rc_dims:\n",
    "    ax2.barh(list(rc_dims.keys()), list(rc_dims.values()), color=\"#e67e22\")\n",
    "    ax2.set_title(\"RandomCombo: Mutations per Dimension\")\n",
    "    ax2.set_xlabel(\"Count\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Show multi-dimensional mutations from RandomCombo\n",
    "print(\"\\nRandomCombo mutations (note multi-dimensional changes):\")\n",
    "for ch in rc_challengers[:6]:\n",
    "    print(f\"  {ch.mutation_note}\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "order(tracker, \"p3_q2_strategy_comparison\",\n",
    "    instruction=\"Rank strategies by ability to find multi-parameter interactions (worst to best):\",\n",
    "    items=[\"NeighborWalk\", \"RandomCombo\", \"LessonGuided\"],\n",
    "    correct_order=[\"NeighborWalk\", \"LessonGuided\", \"RandomCombo\"],\n",
    "    section=\"Pass 3: Strategies\", bloom=\"analyze\",\n",
    "    explanation=\"NeighborWalk: 1 axis only. LessonGuided: focused by rules. RandomCombo: multiple axes, can find synergies.\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Lesson-Guided Search\n",
    "\n",
    "The `LessonGuided` strategy uses rules extracted from previous rungs. Let's create synthetic rules to see how they bias the search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autoresearch_quantum.models import SearchRule, LessonFeedback\n",
    "\n",
    "# Create synthetic rules: prefer z_only verification, avoid x_only\n",
    "synthetic_rules = [\n",
    "    SearchRule(\n",
    "        dimension=\"verification\",\n",
    "        action=\"prefer\",\n",
    "        value=\"z_only\",\n",
    "        confidence=0.8,\n",
    "        reason=\"synthetic: z_only performed best in testing\",\n",
    "    ),\n",
    "    SearchRule(\n",
    "        dimension=\"verification\",\n",
    "        action=\"avoid\",\n",
    "        value=\"x_only\",\n",
    "        confidence=0.7,\n",
    "        reason=\"synthetic: x_only consistently underperformed\",\n",
    "    ),\n",
    "    SearchRule(\n",
    "        dimension=\"encoder_style\",\n",
    "        action=\"prefer\",\n",
    "        value=\"cx_chain\",\n",
    "        confidence=0.6,\n",
    "        reason=\"synthetic: cx_chain has fewer two-qubit gates after transpilation\",\n",
    "    ),\n",
    "]\n",
    "\n",
    "synthetic_feedback = LessonFeedback(\n",
    "    rung=0,\n",
    "    rules=synthetic_rules,\n",
    "    narrowed_dimensions={},\n",
    "    best_spec_fields={},\n",
    ")\n",
    "\n",
    "lg = LessonGuided(num_candidates=8)\n",
    "lg_challengers = lg.generate(\n",
    "    incumbent_for_test, space_for_test, set(), lessons=[synthetic_feedback]\n",
    ")\n",
    "\n",
    "print(f\"LessonGuided generated {len(lg_challengers)} challengers:\")\n",
    "for ch in lg_challengers:\n",
    "    print(f\"  {ch.mutation_note}\")\n",
    "\n",
    "# Check: does it avoid x_only?\n",
    "has_x_only = any(\"x_only\" in ch.mutation_note for ch in lg_challengers)\n",
    "has_z_only = any(\"z_only\" in ch.mutation_note for ch in lg_challengers)\n",
    "print(f\"\\nContains x_only mutations: {has_x_only} (should be rare/absent)\")\n",
    "print(f\"Contains z_only mutations: {has_z_only} (should be common)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#9997; Code Challenge\n",
    "\n",
    "Create a search rule that **avoids** the `\"none\"` postselection setting\n",
    "with high confidence, and add it to the `synthetic_rules` list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** Lesson-guided search closes the feedback loop. What the ratchet learns in one rung can guide exploration in the next, avoiding settings that already proved poor and focusing on promising regions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7 Cross-Rung Propagation\n",
    "\n",
    "The full `run_ratchet()` method runs multiple rungs in sequence. The winner from rung 1 bootstraps rung 2, and lessons accumulate across rungs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rung2_config = load_rung_config(\"../../configs/rungs/rung2.yaml\")\n",
    "\n",
    "# Use smaller budgets for speed\n",
    "config_r1 = replace(rung1_config, step_budget=2, patience=2)\n",
    "config_r2 = replace(rung2_config, step_budget=2, patience=2)\n",
    "\n",
    "store_cross = ResearchStore(tempfile.mkdtemp())\n",
    "harness_cross = AutoresearchHarness(store_cross)\n",
    "\n",
    "print(\"Running cross-rung ratchet (rung1 -> rung2)...\")\n",
    "results = harness_cross.run_ratchet([config_r1, config_r2])\n",
    "\n",
    "for lesson_obj, fb in results:\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"Rung {lesson_obj.rung}: {lesson_obj.name}\")\n",
    "    print(f\"  What helped:    {lesson_obj.what_helped[:2]}\")\n",
    "    print(f\"  What hurt:      {lesson_obj.what_hurt[:2]}\")\n",
    "    print(f\"  Rules learned:  {len(fb.rules)}\")\n",
    "    if fb.best_spec_fields:\n",
    "        print(f\"  Best spec seed: {fb.best_spec_fields.get('seed_style', '?')}\")\n",
    "        print(f\"  Best spec verif: {fb.best_spec_fields.get('verification', '?')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.8 Transfer Evaluation\n",
    "\n",
    "How well does a winning spec perform across different backend noise profiles? The `TransferEvaluator` tests a single spec on multiple backends and reports the pessimistic (minimum) score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autoresearch_quantum.execution.transfer import TransferEvaluator\n",
    "\n",
    "# Get the rung1 winner\n",
    "winner_id = store_cross.load_incumbent_id(1)\n",
    "winner_data = store_cross.load_experiment(1, winner_id)\n",
    "winner_spec = ExperimentSpec(**{\n",
    "    k: tuple(v) if k == \"initial_layout\" and isinstance(v, list) else v\n",
    "    for k, v in winner_data[\"spec\"].items()\n",
    "})\n",
    "\n",
    "# Evaluate on a single additional backend\n",
    "transfer_eval = TransferEvaluator()\n",
    "report = transfer_eval.evaluate_across_backends(\n",
    "    winner_spec,\n",
    "    backends=[\"fake_brisbane\", \"fake_kyoto\"],\n",
    "    rung_config=config_r1,\n",
    ")\n",
    "\n",
    "print(f\"Transfer Evaluation Report\")\n",
    "print(f\"{'='*50}\")\n",
    "print(f\"Spec fingerprint: {winner_spec.fingerprint()}\")\n",
    "print(f\"\\nPer-backend scores:\")\n",
    "for bk, sc in report.per_backend_scores.items():\n",
    "    print(f\"  {bk:20s}: {sc:.6f}\")\n",
    "print(f\"\\nMean score:     {report.mean_score:.6f}\")\n",
    "print(f\"Min score:      {report.min_score:.6f}\")\n",
    "print(f\"Max score:      {report.max_score:.6f}\")\n",
    "print(f\"Std score:      {report.std_score:.6f}\")\n",
    "print(f\"Transfer score: {report.transfer_score:.6f} (pessimistic = min)\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "quiz(tracker, \"p3_q3_transfer\",\n",
    "    question=\"A spec scores 0.8 on fake_brisbane but 0.3 on a different backend. What does this tell you?\",\n",
    "    options=[\n",
    "        \"The spec is bad\",\n",
    "        \"The spec is overfitted to fake_brisbane's specific noise profile\",\n",
    "        \"The other backend is broken\",\n",
    "    ],\n",
    "    correct=1, section=\"Pass 3: Transfer\", bloom=\"evaluate\",\n",
    "    explanation=\"A large score drop on transfer means the settings were tuned to one backend's quirks rather than being generally good.\")\n",
    "\n",
    "checkpoint_summary(tracker, \"Pass 3: Transfer\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Key Insight:** A spec that scores well on one backend may score poorly on another. The transfer score (minimum across backends) prevents overfitting to a single noise profile. This is crucial for real quantum computing where hardware changes day to day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.9 Your Own Experiment\n",
    "\n",
    "Here is a template for you to create and evaluate your own experiment. Modify the spec fields, run it, and see how your design compares to the ratchet's winner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# === YOUR EXPERIMENT ===\n",
    "# Modify these parameters and re-run the cell!\n",
    "\n",
    "my_spec = ExperimentSpec(\n",
    "    rung=1,\n",
    "    seed_style=\"ry_rz\",          # Try: \"h_p\", \"ry_rz\", \"u_magic\"\n",
    "    encoder_style=\"cx_chain\",     # Try: \"cx_chain\", \"cz_compiled\"\n",
    "    verification=\"both\",          # Try: \"both\", \"z_only\", \"x_only\"\n",
    "    postselection=\"all_measured\", # Try: \"all_measured\", \"z_only\", \"none\"\n",
    "    ancilla_strategy=\"dedicated_pair\",  # Try: \"dedicated_pair\", \"reused_single\"\n",
    "    optimization_level=2,         # Try: 1, 2, 3\n",
    "    layout_method=\"sabre\",\n",
    "    routing_method=\"sabre\",\n",
    "    target_backend=\"fake_brisbane\",\n",
    "    noise_backend=\"fake_brisbane\",\n",
    "    shots=256,\n",
    "    repeats=1,\n",
    ")\n",
    "\n",
    "my_result = executor.evaluate(my_spec, rung1_config)\n",
    "\n",
    "print(f\"Your Experiment Results\")\n",
    "print(f\"{'='*50}\")\n",
    "print(f\"Score:            {my_result.score:.6f}\")\n",
    "print(f\"Quality:          {my_result.quality_estimate:.6f}\")\n",
    "print(f\"Acceptance rate:  {my_result.metrics.acceptance_rate:.4f}\")\n",
    "print(f\"Magic witness:    {my_result.metrics.logical_magic_witness:.4f}\")\n",
    "print(f\"Spectator Z:      {my_result.metrics.spectator_logical_z:.4f}\")\n",
    "print(f\"Two-qubit gates:  {my_result.metrics.two_qubit_count}\")\n",
    "print(f\"Depth:            {my_result.metrics.depth}\")\n",
    "print(f\"Total cost:       {my_result.metrics.total_cost:.4f}\")\n",
    "print(f\"Failure mode:     {my_result.metrics.dominant_failure_mode}\")\n",
    "\n",
    "# Compare to the ratchet winner\n",
    "print(f\"\\nRatchet winner score: {winner_data['final_score']:.6f}\")\n",
    "delta = my_result.score - winner_data['final_score']\n",
    "print(f\"Your delta:           {delta:+.6f} {'(you win!)' if delta > 0 else '(ratchet wins)'}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#9997; Code Challenge\n",
    "\n",
    "Modify the experiment spec above, evaluate it, and check whether your custom spec\n",
    "beats the rung1 default incumbent score. Set `my_spec_beats_default` to `True` if it does."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "You have now seen the same system three times.\n",
    "\n",
    "**The first time was magic** -- you pushed a button and an optimizer ran, producing scores, winners, and a lesson narrative. You had no idea what any of it meant.\n",
    "\n",
    "**The second time was science** -- you built the T-state from scratch, understood stabilizers, measured the witness, computed the score by hand, and saw how challengers compete. Every number from Pass 1 had a concrete physical meaning.\n",
    "\n",
    "**The third time was engineering** -- you modified the search space, compared scoring functions, ran multi-rung optimizations, tested transfer across backends, and designed your own experiment.\n",
    "\n",
    "This is the spiral: each pass through the same material reveals structure that was invisible before. The ratchet itself works the same way -- each step builds on what came before, ratcheting toward better and better quantum experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Final Assessment"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "tracker.dashboard()\n",
    "path = tracker.save()\n",
    "print(f\"\\nProgress saved to: {path}\")"
   ],
   "outputs": [],
   "execution_count": null
  }
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