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autoresearch-quantum/notebooks/plan_c/00_dashboard.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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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoresearch Quantum \u2014 Control Room\n",
"\n",
"This interactive dashboard lets you run encoded magic-state experiments with different parameter settings\n",
"and instantly visualize the results. Use it alongside the three Track notebooks:\n",
"\n",
"- **Track A** (Physics): `track_a_physics.ipynb`\n",
"- **Track B** (Engineering): `track_b_engineering.ipynb`\n",
"- **Track C** (Search): `track_c_search.ipynb`\n",
"\n",
"Each track will suggest \"Dashboard Exercises\" that bring you back here for hands-on exploration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import tempfile\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, clear_output, HTML\n",
"\n",
"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",
"from autoresearch_quantum.experiments.encoded_magic_state import build_circuit_bundle\n",
"from autoresearch_quantum.models import (\n",
" ExperimentSpec, RungConfig, EvaluationMetrics, TierResult,\n",
" QualityWeights, CostWeights, ScoreConfig, SearchSpaceConfig,\n",
" TierPolicyConfig, HardwareConfig,\n",
")\n",
"from autoresearch_quantum.execution.local import LocalCheapExecutor\n",
"from autoresearch_quantum.execution.backends import resolve_backend, backend_metadata\n",
"from autoresearch_quantum.execution.transpile import (\n",
" transpile_circuits, count_two_qubit_gates, runtime_estimate, circuit_metadata,\n",
")\n",
"from autoresearch_quantum.scoring.score import score_metrics\n",
"from autoresearch_quantum.config import load_rung_config\n",
"\n",
"print(\"All imports successful.\")"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"from autoresearch_quantum.teaching import LearningTracker\n",
"from autoresearch_quantum.teaching.assess import quiz, predict_choice, reflect, checkpoint_summary\n",
"tracker = LearningTracker(\"plan_c_dashboard\")\n",
"print(\"Learning tracker active.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup: Load Base Configuration\n",
"\n",
"We load the rung-1 configuration as a baseline. The dashboard widgets will override individual parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load rung-1 config as our baseline\n",
"rung_config = load_rung_config(\"../../configs/rungs/rung1.yaml\")\n",
"executor = LocalCheapExecutor()\n",
"\n",
"# Storage for comparison runs\n",
"run_history = []\n",
"\n",
"print(f\"Loaded config: {rung_config.name}\")\n",
"print(f\"Objective: {rung_config.objective}\")"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"quiz(tracker, \"q1_baseline\",\n",
" question=\"Why does the dashboard start from a rung-1 config as baseline?\",\n",
" options=[\n",
" \"It is the only config that exists\",\n",
" \"It provides sensible defaults that widgets then override one at a time\",\n",
" \"Higher rungs require IBM hardware access\",\n",
" ],\n",
" correct=1, section=\"1. Setup\", bloom=\"understand\",\n",
" explanation=\"The rung-1 config defines the full parameter space and bootstrap incumbent. Widgets let you explore variations.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interactive Dashboard\n",
"\n",
"Adjust the widgets below and click **Run Experiment** to execute a single experiment with the chosen\n",
"parameters. Click **Compare** to overlay a second run on the same plots (in orange) for side-by-side\n",
"comparison.\n",
"\n",
"**Tip**: Try changing `seed_style` among `h_p`, `ry_rz`, `u_magic` \u2014 the witness should stay roughly\n",
"the same (they are equivalent preparations up to global phase)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now run three experiments using the dashboard above:\n",
"1. Set seed_style to `h_p`, click **Run Experiment**\n",
"2. Change to `ry_rz`, click **Compare**\n",
"3. Change to `u_magic`, click **Compare**\n",
"\n",
"Look at the \"Quality Metrics\" panel. Record what you see, then verify your prediction below."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"predict_choice(tracker, \"q2_verification_effect\",\n",
" question=\"What happens to acceptance rate if you set verification to 'none'?\",\n",
" options=[\n",
" \"Acceptance rate drops because there are no checks\",\n",
" \"Acceptance rate goes to 100% because no shots are filtered\",\n",
" \"No change \\u2014 verification doesn't affect acceptance\",\n",
" ],\n",
" correct=1, section=\"2. Exploration\", bloom=\"apply\",\n",
" explanation=\"With verification='none', there are no syndrome checks, so ALL shots are accepted (100%). But quality may be lower.\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"reflect(tracker, \"q3_tradeoff\",\n",
" question=\"After trying several parameter combinations, what tension do you notice between quality and acceptance?\",\n",
" section=\"2. Exploration\", bloom=\"analyze\",\n",
" model_answer=\"Stricter verification improves quality by filtering errors, but reduces acceptance rate. The score balances this: score = quality \\u00d7 acceptance / cost.\")\n",
"checkpoint_summary(tracker, \"2. Exploration\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# \u2500\u2500 Widget definitions \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
"w_seed = widgets.Dropdown(\n",
" options=[\"h_p\", \"ry_rz\", \"u_magic\"],\n",
" value=\"h_p\",\n",
" description=\"Seed style:\",\n",
")\n",
"w_encoder = widgets.Dropdown(\n",
" options=[\"cx_chain\", \"cz_compiled\"],\n",
" value=\"cx_chain\",\n",
" description=\"Encoder:\",\n",
")\n",
"w_verification = widgets.Dropdown(\n",
" options=[\"both\", \"z_only\", \"x_only\", \"none\"],\n",
" value=\"both\",\n",
" description=\"Verification:\",\n",
")\n",
"w_postselection = widgets.Dropdown(\n",
" options=[\"all_measured\", \"z_only\", \"x_only\", \"none\"],\n",
" value=\"all_measured\",\n",
" description=\"Postselect:\",\n",
")\n",
"w_opt_level = widgets.IntSlider(\n",
" value=2, min=1, max=3, step=1,\n",
" description=\"Opt level:\",\n",
")\n",
"w_shots = widgets.IntSlider(\n",
" value=256, min=64, max=1024, step=64,\n",
" description=\"Shots:\",\n",
")\n",
"w_backend = widgets.Dropdown(\n",
" options=[\"fake_brisbane\"],\n",
" value=\"fake_brisbane\",\n",
" description=\"Backend:\",\n",
")\n",
"\n",
"btn_run = widgets.Button(description=\"Run Experiment\", button_style=\"primary\")\n",
"btn_compare = widgets.Button(description=\"Compare\", button_style=\"warning\")\n",
"btn_clear = widgets.Button(description=\"Clear History\", button_style=\"danger\")\n",
"\n",
"out = widgets.Output()\n",
"out_text = widgets.Output()\n",
"\n",
"\n",
"def _build_spec():\n",
" \"\"\"Build an ExperimentSpec from the current widget values.\"\"\"\n",
" return ExperimentSpec(\n",
" rung=1,\n",
" seed_style=w_seed.value,\n",
" encoder_style=w_encoder.value,\n",
" verification=w_verification.value,\n",
" postselection=w_postselection.value,\n",
" ancilla_strategy=\"dedicated_pair\",\n",
" optimization_level=w_opt_level.value,\n",
" layout_method=\"sabre\",\n",
" routing_method=\"sabre\",\n",
" approximation_degree=1.0,\n",
" target_backend=w_backend.value,\n",
" noise_backend=w_backend.value,\n",
" shots=w_shots.value,\n",
" repeats=1,\n",
" )\n",
"\n",
"\n",
"def _run_and_collect():\n",
" \"\"\"Run experiment, return (spec, tier_result, prep_circuit, bundle).\"\"\"\n",
" spec = _build_spec()\n",
" result = executor.evaluate(spec, rung_config)\n",
" bundle = build_circuit_bundle(spec)\n",
" backend = resolve_backend(spec.target_backend, rung_config.hardware)\n",
" transpiled = transpile_circuits([bundle.acceptance], spec, backend)[0]\n",
" return spec, result, bundle.prep, transpiled\n",
"\n",
"\n",
"def _label(spec):\n",
" return f\"{spec.seed_style}/{spec.encoder_style}/opt{spec.optimization_level}\"\n",
"\n",
"\n",
"def _plot_all(clear=True):\n",
" \"\"\"Draw the 2x2 dashboard from run_history.\"\"\"\n",
" with out:\n",
" if clear:\n",
" clear_output(wait=True)\n",
"\n",
" if not run_history:\n",
" print(\"No runs yet. Click 'Run Experiment'.\")\n",
" return\n",
"\n",
" colors = plt.cm.tab10.colors\n",
" fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
" fig.suptitle(\"Autoresearch Quantum \u2014 Control Room\", fontsize=14, fontweight=\"bold\")\n",
"\n",
" # \u2500\u2500 Top-left: Circuit diagram (latest run only) \u2500\u2500\n",
" ax_circ = axes[0, 0]\n",
" ax_circ.set_axis_off()\n",
" latest_prep = run_history[-1][2]\n",
" latest_label = _label(run_history[-1][0])\n",
" ax_circ.set_title(f\"Preparation Circuit ({latest_label})\", fontsize=11)\n",
" # Draw circuit as text\n",
" circ_text = latest_prep.draw(output=\"text\").__str__()\n",
" ax_circ.text(\n",
" 0.02, 0.95, circ_text,\n",
" transform=ax_circ.transAxes, fontsize=7, verticalalignment=\"top\",\n",
" fontfamily=\"monospace\",\n",
" bbox=dict(boxstyle=\"round\", facecolor=\"#f0f0f0\", alpha=0.8),\n",
" )\n",
"\n",
" # \u2500\u2500 Top-right: Histogram of acceptance counts \u2500\u2500\n",
" ax_hist = axes[0, 1]\n",
" ax_hist.set_title(\"Measurement Outcome Counts (acceptance circuit)\", fontsize=11)\n",
" bar_width = 0.8 / max(len(run_history), 1)\n",
" for i, (spec, result, _, _) in enumerate(run_history):\n",
" counts = result.counts_summary.get(\"acceptance\", {}).get(\"latest\", {})\n",
" raw = counts.get(\"raw_data_counts\", {})\n",
" if raw:\n",
" keys = sorted(raw.keys())\n",
" vals = [raw[k] for k in keys]\n",
" x = np.arange(len(keys))\n",
" ax_hist.bar(\n",
" x + i * bar_width, vals, bar_width,\n",
" label=_label(spec), color=colors[i % len(colors)], alpha=0.8,\n",
" )\n",
" if i == len(run_history) - 1:\n",
" ax_hist.set_xticks(x + bar_width * (len(run_history) - 1) / 2)\n",
" ax_hist.set_xticklabels(keys, rotation=90, fontsize=6)\n",
" ax_hist.set_ylabel(\"Counts\")\n",
" ax_hist.legend(fontsize=8)\n",
"\n",
" # \u2500\u2500 Bottom-left: Quality metrics bar chart \u2500\u2500\n",
" ax_metrics = axes[1, 0]\n",
" ax_metrics.set_title(\"Quality Metrics\", fontsize=11)\n",
" metric_names = [\"acceptance_rate\", \"witness\", \"score\"]\n",
" x = np.arange(len(metric_names))\n",
" bar_w = 0.8 / max(len(run_history), 1)\n",
" for i, (spec, result, _, _) in enumerate(run_history):\n",
" m = result.metrics\n",
" vals = [\n",
" m.acceptance_rate,\n",
" m.logical_magic_witness or 0.0,\n",
" result.score,\n",
" ]\n",
" ax_metrics.barh(\n",
" x + i * bar_w, vals, bar_w,\n",
" label=_label(spec), color=colors[i % len(colors)], alpha=0.8,\n",
" )\n",
" ax_metrics.set_yticks(x + bar_w * (len(run_history) - 1) / 2)\n",
" ax_metrics.set_yticklabels(metric_names)\n",
" ax_metrics.set_xlim(0, 1.0)\n",
" ax_metrics.legend(fontsize=8)\n",
"\n",
" # \u2500\u2500 Bottom-right: Transpile cost stats \u2500\u2500\n",
" ax_cost = axes[1, 1]\n",
" ax_cost.set_title(\"Transpile & Cost Stats\", fontsize=11)\n",
" cost_names = [\"2Q gates\", \"depth\", \"total_cost\"]\n",
" x = np.arange(len(cost_names))\n",
" bar_w = 0.8 / max(len(run_history), 1)\n",
" for i, (spec, result, _, _) in enumerate(run_history):\n",
" m = result.metrics\n",
" vals = [m.two_qubit_count, m.depth, m.total_cost]\n",
" ax_cost.bar(\n",
" x + i * bar_w, vals, bar_w,\n",
" label=_label(spec), color=colors[i % len(colors)], alpha=0.8,\n",
" )\n",
" ax_cost.set_xticks(x + bar_w * (len(run_history) - 1) / 2)\n",
" ax_cost.set_xticklabels(cost_names)\n",
" ax_cost.legend(fontsize=8)\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"\n",
"def _print_summary():\n",
" \"\"\"Print textual summary of the latest run.\"\"\"\n",
" with out_text:\n",
" clear_output(wait=True)\n",
" if not run_history:\n",
" return\n",
" spec, result, _, _ = run_history[-1]\n",
" m = result.metrics\n",
" print(\"=\" * 60)\n",
" print(f\" Run #{len(run_history)}: {_label(spec)}\")\n",
" print(\"=\" * 60)\n",
" print(f\" Score: {result.score:.4f}\")\n",
" print(f\" Quality estimate: {result.quality_estimate:.4f}\")\n",
" print(f\" Acceptance rate: {m.acceptance_rate:.4f}\")\n",
" print(f\" Magic witness: {m.logical_magic_witness:.4f}\" if m.logical_magic_witness else \" Magic witness: N/A\")\n",
" print(f\" Ideal fidelity: {m.ideal_encoded_fidelity:.4f}\" if m.ideal_encoded_fidelity else \" Ideal fidelity: N/A\")\n",
" print(f\" Noisy fidelity: {m.noisy_encoded_fidelity:.4f}\" if m.noisy_encoded_fidelity else \" Noisy fidelity: N/A\")\n",
" print(f\" Spectator Z: {m.spectator_logical_z:.4f}\" if m.spectator_logical_z else \" Spectator Z: N/A\")\n",
" print(f\" Stability score: {m.stability_score:.4f}\" if m.stability_score else \" Stability score: N/A\")\n",
" print(f\" Two-qubit gates: {m.two_qubit_count}\")\n",
" print(f\" Circuit depth: {m.depth}\")\n",
" print(f\" Total cost: {m.total_cost:.4f}\")\n",
" print(f\" Failure mode: {m.dominant_failure_mode}\")\n",
" print(\"=\" * 60)\n",
"\n",
"\n",
"def on_run(btn):\n",
" \"\"\"Run a fresh experiment (replaces history).\"\"\"\n",
" run_history.clear()\n",
" spec, result, prep, transpiled = _run_and_collect()\n",
" run_history.append((spec, result, prep, transpiled))\n",
" _plot_all(clear=True)\n",
" _print_summary()\n",
"\n",
"\n",
"def on_compare(btn):\n",
" \"\"\"Run experiment and overlay on existing plots.\"\"\"\n",
" spec, result, prep, transpiled = _run_and_collect()\n",
" run_history.append((spec, result, prep, transpiled))\n",
" _plot_all(clear=True)\n",
" _print_summary()\n",
"\n",
"\n",
"def on_clear(btn):\n",
" \"\"\"Clear all history.\"\"\"\n",
" run_history.clear()\n",
" with out:\n",
" clear_output(wait=True)\n",
" print(\"History cleared.\")\n",
" with out_text:\n",
" clear_output(wait=True)\n",
"\n",
"\n",
"btn_run.on_click(on_run)\n",
"btn_compare.on_click(on_compare)\n",
"btn_clear.on_click(on_clear)\n",
"\n",
"# \u2500\u2500 Layout \u2500\u2500\n",
"controls_left = widgets.VBox([w_seed, w_encoder, w_verification, w_postselection])\n",
"controls_right = widgets.VBox([w_opt_level, w_shots, w_backend])\n",
"buttons = widgets.HBox([btn_run, btn_compare, btn_clear])\n",
"control_panel = widgets.HBox([controls_left, controls_right])\n",
"\n",
"display(widgets.VBox([\n",
" widgets.HTML(\"<h3>Experiment Controls</h3>\"),\n",
" control_panel,\n",
" buttons,\n",
" out,\n",
" out_text,\n",
"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Dashboard Exercise:**\n",
"1. Set verification=`both`, postselection=`all_measured`. Click **Run Experiment**.\n",
"2. Set verification=`none`, postselection=`none`. Click **Compare**.\n",
"\n",
"Compare the acceptance rates and witness values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to Use This Dashboard\n",
"\n",
"1. **Single run**: Adjust parameters, click **Run Experiment**. All four panels update.\n",
"2. **Compare**: Change a parameter and click **Compare**. The new result overlays in a different color.\n",
"3. **Clear**: Click **Clear History** to reset.\n",
"\n",
"### Suggested Explorations\n",
"\n",
"| Experiment | What to change | What to watch |\n",
"|---|---|---|\n",
"| Seed equivalence | `seed_style`: h_p, ry_rz, u_magic | Witness should be stable |\n",
"| Encoder comparison | `encoder_style`: cx_chain vs cz_compiled | Gate count differs |\n",
"| Optimization impact | `optimization_level`: 1 vs 3 | Gate count drops, score rises |\n",
"| Verification effect | `verification`: both vs none | Acceptance rate changes |\n",
"| Shot noise | `shots`: 64 vs 512 | Metrics variance decreases |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Dashboard Exercise:**\n",
"1. Set opt_level=1. Click **Run Experiment**.\n",
"2. Set opt_level=3. Click **Compare**.\n",
"3. Look at BOTH the quality metrics AND the cost stats panels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Free Response: Dashboard Synthesis\n",
"\n",
"Now that you have explored seeds, verification, and optimization levels, reflect on the relationships."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Learning Dashboard"
]
},
{
"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",
"source": "---\n## Navigation \u2014 Plan C\n\n**Recommended reading order:**\n1. You are here: **Dashboard** (keep open alongside tracks)\n2. [Track A \u2014 Physics](track_a_physics.ipynb): the quantum error-detecting code\n3. [Track B \u2014 Engineering](track_b_engineering.ipynb): noise, transpilation, cost\n4. [Track C \u2014 Search](track_c_search.ipynb): optimisation and the ratchet\n\n*\u2190 Back to [Start Here](../00_START_HERE.ipynb)*",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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