Commit graph

4 commits

Author SHA1 Message Date
e13a3268c2 Add teaching notebooks, widget-based quizzes, bug fixes, and expanded tests
- 8 Jupyter notebooks across 3 learning plans (A: bottom-up, B: spiral, C: parallel tracks)
- Teaching toolkit (src/autoresearch_quantum/teaching/) with ipywidgets-based
  quiz, predict_choice, reflect, and order widgets — visually distinct from code cells
- Fix spectator_z operator: was {1:'Z',2:'Z'} (IZZI, expectation=0), now {1:'Z',3:'Z'}
  (ZIZI, expectation=+1 for ideal T-state, commutes with logical operators)
- Fix u_magic seed: swap phase arguments to match h_p and ry_rz preparations
- Fix double-display bug: widgets rendered twice when function returned the box
- Fix CLI override parser for negative integers and missing '=' validation
- Fix stabilizer detection quiz: ZZZZ detects X errors, not Z errors
- Add ties parameter to order() for questions with interchangeable items
- Expand test suite from 21 to 107 tests
- Update README with notebook instructions and project tree
2026-04-07 17:14:37 +02:00
2cce5af994 Fix README: replace absolute local paths with relative links
The two links to hardware.py and four_two_two.py pointed at
/Users/oho/... which GitHub cannot resolve, causing the 404
Octocat page to render on top. Also updated the project tree
to reflect new files (strategies.py, feedback.py, transfer.py,
rung5.yaml, paper/).
2026-04-05 12:37:39 +02:00
51d0e5f26b Add technical paper: 19-page LaTeX document with compiled PDF
Covers the quantum mechanics (stabilisers, witness function, scoring),
the autoresearch engine (ratchet, strategies, lesson feedback, propagation),
all 21 test claims with falsification conditions, and a standalone usage guide.
2026-04-05 12:37:39 +02:00
f9b8f3457f Initial commit: autoresearch-quantum — automated magic-state preparation ratchet
Karpathy-style autoresearch engine for encoded magic-state preparation
on the [[4,2,2]] quantum error-detecting code using Qiskit Aer simulation.

Five-rung progressive search: baseline -> stability -> transfer -> factory -> Rosenfeld.
Smart challenger generation (neighbor walk + random combo + lesson-guided).
Machine-readable lesson feedback with per-dimension effects, interaction detection,
and cross-rung propagation. Factory throughput scoring. Resumable execution.
21 tests, all passing.
2026-04-05 12:37:39 +02:00