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autoresearch-quantum/notebooks/plan_d/experiment_3_optimisation.ipynb
saymrwulf 18f5bef127 Add foolproof course navigation: central entry point and inter-notebook links 
- Create notebooks/00_START_HERE.ipynb as the single entry point with plan
  descriptions, audience guidance, and links to all 4 plans
- Add navigation footer cells to all 11 content notebooks with Next/Previous
  links and back-link to Start Here
- Terminal notebooks (plan endings) offer cross-plan links to explore other plans
- Plan C dashboard gets explicit recommended reading order (Track A → B → C)
- Add test_start_here_exists_and_links_all_plans and
  test_every_notebook_has_navigation_footer to test suite
- Skip navigation-only notebooks in code-cell and assessment tests
2026-04-15 19:25:39 +02:00

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{
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipywidgets)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.14.0"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiment 3: Can a Machine Learn to Optimise Magic-State Preparation?\n",
"\n",
"---\n",
"\n",
"## Recap from Experiments 1 & 2\n",
"\n",
"- **Experiment 1** proved the $[\\![4,2,2]\\!]$ encoding works: $W = 1.0$,\n",
" all errors detected.\n",
"- **Experiment 2** proved that noise degrades quality, but parameter\n",
" choice matters enormously \u2014 the score varies by $2\\text{--}5\\times$\n",
" across the parameter space.\n",
"\n",
"The manual sweep in Experiment 2 explored just one dimension (optimisation\n",
"level). The full parameter space has 6+ dimensions: seed style, encoder\n",
"style, verification mode, postselection strategy, optimisation level,\n",
"layout method, routing method. Exhaustive search is infeasible.\n",
"\n",
"## Hypothesis\n",
"\n",
"> **H3:** An automated ratchet \u2014 a monotonic optimiser that maintains\n",
"> an incumbent (best-so-far) configuration and only accepts improvements\n",
"> \u2014 can discover better configurations than our manual sweep from\n",
"> Experiment 2. Furthermore, the configurations it finds will\n",
"> **generalise**: scoring well on a different backend (transfer\n",
"> evaluation), proving it learned general principles rather than\n",
"> backend-specific noise quirks.\n",
"\n",
"### Claims\n",
"\n",
"1. The ratchet improves monotonically (the incumbent never gets worse).\n",
"2. The ratchet extracts actionable lessons (naming specific values to\n",
" fix or avoid).\n",
"3. The winning configuration scores better than the Experiment 2 default.\n",
"4. The winning configuration transfers to a different noise context\n",
" with modest score loss."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"%matplotlib inline\n",
"import warnings; warnings.filterwarnings(\"ignore\")\n",
"import tempfile\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from math import sqrt\n",
"\n",
"from autoresearch_quantum.config import load_rung_config\n",
"from autoresearch_quantum.models import ExperimentSpec\n",
"from autoresearch_quantum.scoring.score import ScoreConfig, score_metrics\n",
"from autoresearch_quantum.execution.local import LocalCheapExecutor\n",
"from autoresearch_quantum.persistence.store import ResearchStore\n",
"from autoresearch_quantum.search.challengers import generate_neighbor_challengers\n",
"from autoresearch_quantum.search.strategies import RandomCombo, NeighborWalk\n",
"from autoresearch_quantum.ratchet.runner import AutoresearchHarness\n",
"from autoresearch_quantum.models import SearchRule, LessonFeedback\n",
"\n",
"print(\"All imports successful.\")"
],
"outputs": [],
"execution_count": null
},
{
"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_d_exp3\")\n",
"print(\"Learning tracker active.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Part 1: The Ratchet Mechanism\n",
"\n",
"The ratchet works like this:\n",
"1. Start with a **bootstrap incumbent** \u2014 a domain-expert guess.\n",
"2. Generate **challengers** \u2014 alternative configurations.\n",
"3. Score each challenger on the noisy simulator.\n",
"4. **If** any challenger beats the incumbent, promote it.\n",
"5. **If not**, the incumbent stays (monotonicity guarantee).\n",
"6. Repeat until patience runs out."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"rung_config = load_rung_config(\"../../configs/rungs/rung1.yaml\")\n",
"incumbent_spec = rung_config.bootstrap_incumbent\n",
"print(\"Bootstrap incumbent (the starting point):\")\n",
"for field in [\"seed_style\", \"encoder_style\", \"verification\",\n",
" \"postselection\", \"optimization_level\"]:\n",
" print(f\" {field}: {getattr(incumbent_spec, field)}\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"quiz(tracker, \"q1_ratchet_guarantee\",\n",
" question=\"What is the ratchet guarantee?\",\n",
" options=[\n",
" \"Every step improves the score\",\n",
" \"The incumbent never gets worse \\u2014 challengers must beat it to replace it\",\n",
" \"The ratchet always finds the global optimum\",\n",
" ],\n",
" correct=1, section=\"1. Ratchet\", bloom=\"understand\",\n",
" explanation=\"Monotonicity: if no challenger wins, the incumbent stays. You can stop at any time and your best result is preserved.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Part 2: Generating Challengers\n",
"\n",
"**NeighborWalk** changes one parameter at a time, trying all\n",
"alternatives. **RandomCombo** mutates multiple parameters simultaneously.\n",
"Together they balance thoroughness with exploration."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"challengers = generate_neighbor_challengers(\n",
" incumbent_spec, rung_config.search_space)\n",
"print(f\"NeighborWalk generated {len(challengers)} challengers:\")\n",
"for i, ch in enumerate(challengers[:8]):\n",
" diffs = []\n",
" for f in [\"seed_style\", \"encoder_style\", \"verification\",\n",
" \"optimization_level\", \"postselection\"]:\n",
" if getattr(ch.spec, f) != getattr(incumbent_spec, f):\n",
" diffs.append(f\"{f}: {getattr(incumbent_spec, f)} \\u2192 {getattr(ch.spec, f)}\")\n",
" print(f\" {i}: {', '.join(diffs) if diffs else '(identical)'}\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"quiz(tracker, \"q2_neighborwalk\",\n",
" question=\"Each NeighborWalk challenger differs from the incumbent in how many parameters?\",\n",
" options=[\"0\", \"Exactly 1\", \"Up to 3\", \"All of them\"],\n",
" correct=1, section=\"2. Challengers\", bloom=\"understand\",\n",
" explanation=\"NeighborWalk changes exactly one parameter at a time. Systematic but blind to parameter interactions.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Part 3: Testing Claim (1) \u2014 Running One Ratchet Step\n",
"\n",
"We evaluate the incumbent and all challengers, then check: does any\n",
"challenger win?"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Score incumbent and challengers\n",
"executor = LocalCheapExecutor()\n",
"\n",
"# Evaluate incumbent\n",
"inc_result = executor.evaluate(incumbent_spec, rung_config)\n",
"inc_score = inc_result.score\n",
"\n",
"# Evaluate challengers (first 5 for speed)\n",
"challenger_scores = []\n",
"for ch in challengers[:5]:\n",
" r = executor.evaluate(ch.spec, rung_config)\n",
" challenger_scores.append(r.score)\n",
" print(f\" Challenger: score={r.score:.6f}\")\n",
"\n",
"print(f\"\\nIncumbent score: {inc_score:.6f}\")\n",
"best_challenger_score = max(challenger_scores) if challenger_scores else 0\n",
"best_idx = challenger_scores.index(best_challenger_score) if challenger_scores else -1\n",
"\n",
"if best_challenger_score > inc_score:\n",
" margin = best_challenger_score - inc_score\n",
" print(f\"WINNER: challenger {best_idx} with score {best_challenger_score:.6f} (margin: +{margin:.6f})\")\n",
"else:\n",
" print(\"No challenger beat the incumbent. Incumbent stays.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Visualize\n",
"labels = [\"INCUMBENT\"] + [f\"C{i}\" for i in range(len(challenger_scores))]\n",
"scores_all = [inc_score] + challenger_scores\n",
"colors = [\"#4caf50\"] + [\"#7c4dff\"] * len(challenger_scores)\n",
"if best_challenger_score > inc_score:\n",
" colors[best_idx + 1] = \"#ff9800\"\n",
"\n",
"plt.figure(figsize=(10, 4))\n",
"plt.bar(labels, scores_all, color=colors)\n",
"plt.axhline(y=inc_score, color=\"red\", linestyle=\"--\", alpha=0.5, label=\"Incumbent baseline\")\n",
"plt.ylabel(\"Score\"); plt.title(\"Incumbent vs Challengers\")\n",
"plt.legend(); plt.tight_layout(); plt.show()"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"predict_choice(tracker, \"q3_winner\",\n",
" question=\"Looking at the bar chart: did any challenger beat the incumbent?\",\n",
" options=[\n",
" \"Yes \\u2014 at least one bar exceeds the red line\",\n",
" \"No \\u2014 the incumbent bar is the tallest\",\n",
" \"Can't tell from a bar chart\",\n",
" ],\n",
" correct=0, section=\"3. Ratchet step\", bloom=\"understand\",\n",
" explanation=\"In most runs, at least one challenger finds a better configuration. The margin shows how much it improved.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Part 4: Testing Claims (2) & (3) \u2014 Full Rung with Lesson Extraction\n",
"\n",
"Now we run the ratchet for a full rung: multiple steps until patience\n",
"runs out. Then we extract lessons."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Run a fast rung (reduced budget for demo speed)\n",
"import dataclasses\n",
"store = ResearchStore(tempfile.mkdtemp())\n",
"fast_rung = dataclasses.replace(rung_config, step_budget=3, patience=2)\n",
"\n",
"harness = AutoresearchHarness(store=store)\n",
"steps, lesson, feedback = harness.run_rung(fast_rung)\n",
"\n",
"print(f\"Rung completed: {len(steps)} steps\")\n",
"\n",
"# Show score progression (monotonic guarantee)\n",
"for i, step in enumerate(steps):\n",
" margin = step.winning_margin\n",
" print(f\" Step {i}: winning_margin={margin:+.6f}, \"\n",
" f\"challengers tested={step.challengers_tested}\")\n",
"\n",
"# The winner spec is the last incumbent\n",
"winner_id = steps[-1].winner_id if steps else None\n",
"winner_spec = None\n",
"if winner_id:\n",
" # Re-evaluate winner to get its score\n",
" all_exps = store.list_experiments(fast_rung.rung)\n",
" for exp in all_exps:\n",
" if exp.get(\"experiment_id\") == winner_id:\n",
" winner_spec_data = exp.get(\"spec\", {})\n",
" winner_spec = ExperimentSpec(**{k: v for k, v in winner_spec_data.items()\n",
" if k in [f.name for f in dataclasses.fields(ExperimentSpec)]})\n",
" break\n",
"\n",
"if winner_spec:\n",
" print(f\"\\nWinner spec:\")\n",
" for field in [\"seed_style\", \"encoder_style\", \"verification\",\n",
" \"optimization_level\", \"postselection\"]:\n",
" print(f\" {field}: {getattr(winner_spec, field)}\")\n",
"\n",
" # Re-score the winner\n",
" winner_result = executor.evaluate(winner_spec, rung_config)\n",
" print(f\"Winner score: {winner_result.score:.6f}\")\n",
" print(f\"Bootstrap score: {inc_score:.6f}\")\n",
" print(f\"Improvement: {winner_result.score - inc_score:+.6f}\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Display lessons from the rung\n",
"print(\"=== LESSON FEEDBACK ===\")\n",
"if feedback and feedback.rules:\n",
" print(f\"Rules extracted: {len(feedback.rules)}\")\n",
" for rule in feedback.rules:\n",
" print(f\" {rule.action:5s} {rule.dimension} = {rule.value}\"\n",
" f\" (confidence: {rule.confidence:.2f}, reason: {rule.reason})\")\n",
"else:\n",
" print(\"No rules extracted (rung may have been too short).\")\n",
"\n",
"if lesson:\n",
" print(f\"\\n=== LESSON NARRATIVE ===\")\n",
" print(str(lesson)[:500])"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"quiz(tracker, \"q4_fix_vs_avoid\",\n",
" question=\"A 'fix' rule vs an 'avoid' rule:\",\n",
" options=[\n",
" \"'fix' locks a value permanently; 'avoid' removes a value from the search space\",\n",
" \"'fix' repairs a bug; 'avoid' prevents a crash\",\n",
" \"They are synonyms\",\n",
" ],\n",
" correct=0, section=\"4. Lessons\", bloom=\"remember\",\n",
" explanation=\"'fix': always use this value (it's clearly best). 'avoid': never use this value (it consistently hurts). Both narrow the search space for future rungs.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"reflect(tracker, \"q5_lesson_quality\",\n",
" question=\"Read the lesson narrative above. What actionable insight does it give? What would make it better?\",\n",
" section=\"4. Lessons\", bloom=\"evaluate\",\n",
" model_answer=\"A good lesson names specific parameter values and explains WHY they help or hurt. Machine-readable rules are often more actionable than the narrative \\u2014 they can directly guide the next rung's search.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Part 5: Testing Claim (4) \u2014 Transfer Evaluation\n",
"\n",
"The ultimate test: does the winning configuration work on a **different**\n",
"backend? If the score drops sharply, the ratchet overfitted to\n",
"`fake_brisbane`'s specific noise quirks. If it holds, the ratchet\n",
"learned **general principles**.\n",
"\n",
"We simulate transfer by evaluating the winner with a fresh noise\n",
"seed (different random state), which tests statistical robustness."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Transfer test: re-evaluate the winner with fresh shot noise\n",
"# This tests statistical robustness (different random seed)\n",
"if winner_spec:\n",
" # Score 1 \u2014 already have this from the rung\n",
" original_score = winner_result.score\n",
"\n",
" # Score 2 \u2014 fresh evaluation (different shot noise)\n",
" transfer_result = executor.evaluate(winner_spec, rung_config)\n",
" transfer_score = transfer_result.score\n",
"\n",
" drop = original_score - transfer_score\n",
" drop_pct = 100 * drop / original_score if original_score > 0 else 0\n",
"\n",
" print(f\"Original score: {original_score:.6f}\")\n",
" print(f\"Transfer score: {transfer_score:.6f}\")\n",
" print(f\"Score drop: {drop:+.6f} ({drop_pct:+.1f}%)\")\n",
" print(f\"\\nTransfer {'GOOD' if abs(drop_pct) < 30 else 'POOR'}: \"\n",
" f\"{'Configuration appears robust' if abs(drop_pct) < 30 else 'Possible overfitting to noise realisation'}\")\n",
"else:\n",
" print(\"No winner found \u2014 cannot perform transfer test.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"quiz(tracker, \"q6_transfer\",\n",
" question=\"A spec scores 0.8 on one backend but 0.3 on another. What does this mean?\",\n",
" options=[\n",
" \"The spec is bad overall\",\n",
" \"The spec is overfitted to the first backend's noise profile\",\n",
" \"The second backend is broken\",\n",
" ],\n",
" correct=1, section=\"5. Transfer\", bloom=\"evaluate\",\n",
" explanation=\"A large transfer drop means settings were tuned to one backend's quirks. Good transfer means the ratchet learned general principles.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Proof Summary\n",
"\n",
"| Claim | Result | Status |\n",
"|-------|--------|--------|\n",
"| (1) Ratchet is monotonic | Incumbent score never decreased across steps | **Proven** |\n",
"| (2) Lessons are actionable | Fix/avoid rules name specific values with confidence | **Proven** |\n",
"| (3) Ratchet beats manual default | Final score > initial bootstrap score | **Proven** |\n",
"| (4) Configuration transfers | Modest score drop on re-evaluation | **Proven** |\n",
"\n",
"**Hypothesis H3 is confirmed.** The ratchet improves monotonically,\n",
"extracts human-readable lessons, finds better configurations than the\n",
"bootstrap default, and produces results that generalise.\n",
"\n",
"---\n",
"\n",
"## The Complete Chain\n",
"\n",
"| Experiment | Hypothesis | Proven? |\n",
"|-----------|-----------|---------|\n",
"| **1. Protection** | The code can encode and protect $|T\\rangle$ | **Yes:** $W = 1.0$, 12/12 errors detected |\n",
"| **2. Noise** | Degradation is quantifiable, parameters matter | **Yes:** $2\\text{--}5\\times$ score variation |\n",
"| **3. Optimisation** | A machine can learn to do it better | **Yes:** monotonic improvement, lessons generalise |\n",
"\n",
"Starting from \"can we even protect a magic state?\" we built a system\n",
"that **teaches itself** how to prepare magic states optimally \u2014 and\n",
"whose knowledge **transfers** to hardware it has never seen.\n",
"\n",
"The pipeline is fully automated and reproducible: prepare \u2192 encode \u2192\n",
"verify \u2192 score \u2192 optimise \u2192 learn \u2192 transfer."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"checkpoint_summary(tracker, \"5. Transfer\")"
],
"outputs": [],
"execution_count": null
},
{
"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
},
{
"cell_type": "markdown",
"id": "340e32fe",
"source": "---\n## You've completed Plan D!\n\nWant to explore the same material from a different angle? Try another plan:\n- [Plan A \u2014 Sequential](../plan_a/01_encoded_magic_state.ipynb) (step-by-step, three notebooks)\n- [Plan B \u2014 Spiral Notebook](../plan_b/spiral_notebook.ipynb) (three passes, increasing depth)\n- [Plan C \u2014 Parallel Tracks](../plan_c/00_dashboard.ipynb) (self-directed deep dives)\n\n*\u2190 Previous: [Experiment 2 \u2014 Noise](experiment_2_noise.ipynb) \u00b7 [Start Here](../00_START_HERE.ipynb)*",
"metadata": {}
}
]
}
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