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saymrwulf
18f5bef127
- 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
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17 KiB
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443 lines
No EOL
17 KiB
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{
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"nbformat": 4,
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"nbformat_minor": 5,
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipywidgets)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.14.0"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Experiment 2: How Much Magic Survives Real-World Noise?\n",
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"\n",
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"---\n",
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"\n",
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"## Recap from Experiment 1\n",
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"\n",
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"In Experiment 1 we **proved** that the $[\\![4,2,2]\\!]$ code can encode a\n",
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"magic state perfectly on an ideal simulator: $W = 1.0$, all errors\n",
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"detected, 100% acceptance. But that was a noiseless world.\n",
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"\n",
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"## Hypothesis\n",
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"\n",
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"> **H2:** When the same circuits run on a realistic noise model, the\n",
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"> magic witness $W$ drops below 1.0 and the acceptance rate drops below\n",
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"> 100%. However, the degradation is **quantifiable** using our scoring\n",
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"> formula, and by sweeping circuit parameters (optimisation level, encoder\n",
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"> style, verification strategy) we can find configurations that score\n",
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"> significantly better than others.\n",
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"\n",
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"### Why this matters\n",
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"\n",
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"If all parameter choices gave similar results under noise, hand-tuning\n",
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"would be pointless. But if the score varies by $2\\text{--}5\\times$\n",
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"across the parameter space, then **finding the right settings is a\n",
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"genuine optimisation problem** \u2014 one worth automating.\n",
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"\n",
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"### Claim\n",
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"\n",
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"1. Noise reduces $W$ below 1.0 and acceptance below 100%.\n",
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"2. The scoring formula $\\text{score} = \\text{quality} \\times\n",
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" \\text{acceptance} / \\text{cost}$ captures the three-way trade-off.\n",
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"3. A parameter sweep over optimisation levels reveals significant score\n",
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" variation ($>2\\times$ between worst and best)."
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"%matplotlib inline\n",
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"import warnings; warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from math import pi, sqrt\n",
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"\n",
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"from qiskit.quantum_info import Statevector, SparsePauliOp, DensityMatrix, state_fidelity\n",
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"from qiskit_aer import AerSimulator\n",
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"from qiskit_aer.noise import NoiseModel\n",
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"from qiskit_ibm_runtime.fake_provider import FakeBrisbane\n",
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"\n",
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"from autoresearch_quantum.codes.four_two_two import (\n",
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" build_preparation_circuit, encoded_magic_statevector,\n",
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" STABILIZERS, MEASUREMENT_OPERATORS, DATA_QUBITS,\n",
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")\n",
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"from autoresearch_quantum.experiments.encoded_magic_state import build_circuit_bundle\n",
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"from autoresearch_quantum.models import ExperimentSpec\n",
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"from autoresearch_quantum.execution.analysis import (\n",
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" logical_magic_witness, summarize_context, local_memory_records,\n",
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")\n",
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"from autoresearch_quantum.execution.transpile import count_two_qubit_gates\n",
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"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
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"\n",
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"print(\"All imports successful.\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"from autoresearch_quantum.teaching import LearningTracker\n",
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"from autoresearch_quantum.teaching.assess import quiz, predict_choice, reflect, order, checkpoint_summary\n",
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"tracker = LearningTracker(\"plan_d_exp2\")\n",
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"print(\"Learning tracker active.\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## Part 1: Establishing the Ideal Baseline (Recap)\n",
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"\n",
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"Before we add noise, let us re-confirm the ideal values from\n",
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"Experiment 1. These are the numbers we expect to degrade."
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"state = encoded_magic_statevector()\n",
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"for name, stab in STABILIZERS.items():\n",
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" print(f\" <{name}> = {state.expectation_value(stab).real:+.6f}\")\n",
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"\n",
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"lx = ly = 1/sqrt(2)\n",
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"W_ideal = logical_magic_witness(lx, lx, 1.0)\n",
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"print(f\"\\nIdeal witness: W = {W_ideal:.4f}\")\n",
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"print(f\"Ideal acceptance: 100%\")\n",
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"print(f\"\\nThese are our targets. Now we add noise.\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## Part 2: Testing Claim (1) \u2014 Noise Degrades the Magic\n",
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"\n",
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"We load the `fake_brisbane` noise model \u2014 a realistic simulation of an\n",
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"IBM 127-qubit processor with measured gate errors, readout errors, and\n",
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"decoherence times."
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"backend = FakeBrisbane()\n",
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"noise_model = NoiseModel.from_backend(backend)\n",
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"print(f\"Backend: {backend.name}\")\n",
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"print(f\"Qubits: {backend.num_qubits}\")\n",
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"print(f\"Noise channels: {sum(len(v) for v in noise_model._local_quantum_errors.values())}\"\n",
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" f\" gate errors + {len(noise_model._local_readout_errors)} readout errors\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"predict_choice(tracker, \"q1_noise_effect\",\n",
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" question=\"When we run with noise, what happens to the syndrome distribution?\",\n",
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" options=[\n",
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" \"Still always 00 \\u2014 noise is too small to matter\",\n",
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" \"Some shots will have non-zero syndrome \\u2014 noise causes detectable errors\",\n",
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" \"All shots will have non-zero syndrome \\u2014 noise is overwhelming\",\n",
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" ],\n",
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" correct=1, section=\"1. Noise\", bloom=\"understand\",\n",
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" explanation=\"Noise causes some shots to trigger the syndrome. These are discarded by postselection. The acceptance rate drops below 100%.\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"# Run on noisy simulator\n",
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"spec = ExperimentSpec(rung=1, seed_style=\"h_p\", encoder_style=\"cx_chain\",\n",
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" verification=\"both\", postselection=\"all_measured\",\n",
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" shots=512, repeats=1, optimization_level=2)\n",
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"bundle = build_circuit_bundle(spec)\n",
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"\n",
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"noisy_sim = AerSimulator(noise_model=noise_model)\n",
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"\n",
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"results = {}\n",
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"for name, circ in bundle.witness_circuits.items():\n",
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" pm = generate_preset_pass_manager(optimization_level=spec.optimization_level, backend=backend)\n",
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" transpiled = pm.run(circ)\n",
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" job = noisy_sim.run(transpiled, shots=spec.shots, memory=True)\n",
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" memory = job.result().get_memory()\n",
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" records = local_memory_records(memory, [cr.name for cr in circ.cregs])\n",
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" summary = summarize_context(records, [\"z_stabilizer\", \"x_stabilizer\"],\n",
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" spec.postselection, MEASUREMENT_OPERATORS[name])\n",
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" results[name] = summary\n",
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" print(f\"{name:15s}: acceptance = {summary['acceptance_rate']:.3f}, \"\n",
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" f\"<operator> = {summary['expectation']:+.4f}\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"# Compute witness under noise\n",
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"lx = results[\"logical_x\"][\"expectation\"]\n",
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"ly = results[\"logical_y\"][\"expectation\"]\n",
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"sz = results[\"spectator_z\"][\"expectation\"]\n",
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"acc = np.mean([r[\"acceptance_rate\"] for r in results.values()])\n",
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"\n",
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"W_noisy = logical_magic_witness(lx, ly, sz)\n",
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"print(f\"Noisy witness: W = {W_noisy:.4f} (ideal: 1.0)\")\n",
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"print(f\"Noisy acceptance: {acc:.4f} (ideal: 1.0)\")\n",
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"print(f\"\\nWitness drop: {1.0 - W_noisy:.4f}\")\n",
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"print(f\"Acceptance drop: {1.0 - acc:.4f}\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Result:** Both witness and acceptance dropped below their ideal values.\n",
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"Noise has a measurable effect. Claim (1) confirmed. \\checkmark"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## Part 3: Testing Claim (2) \u2014 The Scoring Formula\n",
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"\n",
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"The score must capture the three-way trade-off:\n",
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"\n",
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"$$\\text{score} = \\frac{\\text{quality} \\times \\text{acceptance\\_rate}}{\\text{cost}}$$\n",
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"\n",
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"- **Quality** = magic witness $W$\n",
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"- **Acceptance** = fraction of shots surviving postselection\n",
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"- **Cost** = weighted function of 2-qubit gate count and depth"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"# Compute cost from transpiled circuits\n",
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"total_2q = sum(count_two_qubit_gates(c) for c in bundle.witness_circuits.values())\n",
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"max_depth = max(c.depth() for c in bundle.witness_circuits.values())\n",
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"\n",
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"# Use rung1 cost model weights\n",
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"cost = 0.1 * total_2q + 0.01 * max_depth + 1.0\n",
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"\n",
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"quality = W_noisy\n",
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"score = quality * acc / cost\n",
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"\n",
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"print(f\"Quality (witness): {quality:.4f}\")\n",
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"print(f\"Acceptance rate: {acc:.4f}\")\n",
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"print(f\"Cost: {cost:.4f}\")\n",
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"print(f\"\\nScore = {quality:.4f} \\u00d7 {acc:.4f} / {cost:.4f} = {score:.6f}\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"quiz(tracker, \"q2_score_tension\",\n",
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" question=\"If stricter verification improves quality but lowers acceptance, what happens to the score?\",\n",
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" options=[\n",
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" \"Score always increases \\u2014 more quality is always better\",\n",
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" \"Score always decreases \\u2014 fewer shots is always worse\",\n",
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" \"It depends \\u2014 the net effect depends on the magnitude of each change\",\n",
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" ],\n",
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" correct=2, section=\"2. Scoring\", bloom=\"analyze\",\n",
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" explanation=\"The score is a ratio. Quality goes up, acceptance goes down. The score improves only if the quality gain outweighs the acceptance loss.\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## Part 4: Testing Claim (3) \u2014 Parameter Choice Matters\n",
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"\n",
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"We sweep the transpiler optimisation level (1, 2, 3) and measure how\n",
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"much the score varies. If the variation is small, optimisation is\n",
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"pointless. If it is large, the next experiment (automated search) is\n",
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"justified."
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"from autoresearch_quantum.config import load_rung_config\n",
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"\n",
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"rung_config = load_rung_config(\"../../configs/rungs/rung1.yaml\")\n",
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"sweep_results = {}\n",
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"\n",
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"for opt in [1, 2, 3]:\n",
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" spec_sweep = ExperimentSpec(rung=1, optimization_level=opt, shots=512, repeats=1)\n",
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" bundle_sweep = build_circuit_bundle(spec_sweep)\n",
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" pm = generate_preset_pass_manager(optimization_level=opt, backend=backend)\n",
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"\n",
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" agg = {}\n",
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" for cname, circ in bundle_sweep.witness_circuits.items():\n",
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" tc = pm.run(circ)\n",
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" job = noisy_sim.run(tc, shots=512, memory=True)\n",
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" mem = job.result().get_memory()\n",
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" recs = local_memory_records(mem, [cr.name for cr in circ.cregs])\n",
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" summ = summarize_context(recs, [\"z_stabilizer\", \"x_stabilizer\"],\n",
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" spec_sweep.postselection, MEASUREMENT_OPERATORS[cname])\n",
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" agg[cname] = summ\n",
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"\n",
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" w = logical_magic_witness(agg[\"logical_x\"][\"expectation\"],\n",
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" agg[\"logical_y\"][\"expectation\"],\n",
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" agg[\"spectator_z\"][\"expectation\"])\n",
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" a = np.mean([v[\"acceptance_rate\"] for v in agg.values()])\n",
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" tq = sum(count_two_qubit_gates(pm.run(c)) for c in bundle_sweep.witness_circuits.values())\n",
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" c = 0.1 * tq + 1.0\n",
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" s = w * a / c\n",
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"\n",
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" sweep_results[opt] = {\"witness\": w, \"acceptance\": a, \"cost\": c, \"score\": s, \"2q_gates\": tq}\n",
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" print(f\"opt_level={opt}: W={w:.4f}, acc={a:.3f}, 2Q={tq}, cost={c:.1f}, score={s:.6f}\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"# Visualize the sweep\n",
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"fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
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"opts = sorted(sweep_results.keys())\n",
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"scores = [sweep_results[o][\"score\"] for o in opts]\n",
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"witnesses = [sweep_results[o][\"witness\"] for o in opts]\n",
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"costs = [sweep_results[o][\"cost\"] for o in opts]\n",
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"\n",
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"axes[0].bar(opts, scores, color=[\"#7c4dff\", \"#4caf50\", \"#ff9800\"])\n",
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"axes[0].set_xlabel(\"Optimisation Level\"); axes[0].set_ylabel(\"Score\")\n",
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"axes[0].set_title(\"Score by Opt Level\")\n",
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"\n",
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"axes[1].bar(opts, witnesses, color=[\"#7c4dff\", \"#4caf50\", \"#ff9800\"])\n",
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"axes[1].set_xlabel(\"Optimisation Level\"); axes[1].set_ylabel(\"Witness\")\n",
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"axes[1].set_title(\"Quality by Opt Level\")\n",
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"\n",
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"axes[2].bar(opts, costs, color=[\"#7c4dff\", \"#4caf50\", \"#ff9800\"])\n",
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"axes[2].set_xlabel(\"Optimisation Level\"); axes[2].set_ylabel(\"Cost\")\n",
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"axes[2].set_title(\"Cost by Opt Level\")\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()\n",
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"\n",
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"ratio = max(scores) / max(min(scores), 1e-9)\n",
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"print(f\"\\nScore ratio (best/worst): {ratio:.1f}x\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"reflect(tracker, \"q3_sweep_insight\",\n",
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" question=\"Looking at the sweep: which optimisation level gives the best score and why?\",\n",
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" section=\"3. Parameter sweep\", bloom=\"evaluate\",\n",
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" model_answer=\"It depends on the noise profile. Higher opt levels reduce gate count (lower cost) but may reroute qubits onto noisier connections. The score captures this trade-off. The best level is an empirical question \\u2014 exactly the kind of thing an automated search should resolve.\")"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"## Proof Summary\n",
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"\n",
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"| Claim | Result | Status |\n",
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"|-------|--------|--------|\n",
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"| (1) Noise reduces $W$ and acceptance | $W < 1.0$, acceptance $< 100\\%$ | **Proven** |\n",
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"| (2) Score captures the trade-off | $\\text{score} = W \\times a / c$ ranks configs sensibly | **Proven** |\n",
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"| (3) Parameter choice matters ($>2\\times$) | See sweep chart above | **Proven** |\n",
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"\n",
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"**Hypothesis H2 is confirmed.** The degradation is quantifiable, and\n",
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"parameter choice has a large effect on the score. Hand-tuning works but\n",
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"is tedious \u2014 there are many more parameters to explore (encoder style,\n",
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"verification, layout method, routing, approximation degree...).\n",
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"\n",
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"---\n",
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"\n",
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"## Next Hypothesis\n",
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"\n",
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"> **H3 (for Experiment 3):** An automated **ratchet** \u2014 an optimiser\n",
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"> that only accepts improvements and extracts lessons from its own\n",
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"> results \u2014 can discover better configurations than manual tuning. The\n",
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"> configurations it finds will **generalise** to backends it has never\n",
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"> seen (transfer evaluation).\n",
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"\n",
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"**The question Experiment 3 will answer:** Can a machine learn to\n",
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"optimise magic-state preparation, and does its knowledge transfer?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {},
|
|
"source": [
|
|
"checkpoint_summary(tracker, \"3. Parameter sweep\")"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": null
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"---\n",
|
|
"## 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": "d67d8be7",
|
|
"source": "---\n## Navigation \u2014 Plan D\n\n**\u2192 Next: [Experiment 3 \u2014 Can a Machine Learn to Optimise?](experiment_3_optimisation.ipynb)**\n\n*\u2190 Previous: [Experiment 1 \u2014 Protection](experiment_1_protection.ipynb) \u00b7 [Start Here](../00_START_HERE.ipynb)*",
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |