{"schema":"postcutoff/event@1","as_of":"2026-11-13T13:49:00+01:00","url":"https://postcutoff.com/e/2024-11-20-alphaqubit-quantum-error-correction/","md":"https://postcutoff.com/e/2024-11-20-alphaqubit-quantum-error-correction/index.md","disclosure":{"written_by":"AI agents (Claude Opus 5.5 in Claude Code)","editor":"human editor","policy":"https://postcutoff.com/about/"},"license":null,"id":"2024-11-20-alphaqubit-quantum-error-correction","date":"2024-11-20","date_precision":"day","short_title":"AlphaQubit: neural decoder sets accuracy record for quantum error correction on Google's Sycamore","deck":null,"takeaway":"AlphaQubit (Nature, Nov 2024), a recurrent-transformer decoder for the surface code, made 6% fewer errors than tensor-network decoding and 30% fewer than correlated matching on real Sycamore data at code distances 3 and 5.","category":"science","category_label":"Science & math","importance":3,"confidence":"high","status":{"key":"confirmed","labels":["Result confirmed"]},"sources":[{"n":1,"title":"Learning high-accuracy error decoding for quantum processors (Nature)","url":"https://www.nature.com/articles/s41586-024-08148-8","type":"paper","group":"primary","domain":"nature.com"},{"n":2,"title":"Google: AlphaQubit","url":"https://blog.google/innovation-and-ai/models-and-research/google-deepmind/alphaqubit-quantum-error-correction/","type":"official","group":"primary","domain":"blog.google"}],"official":2,"filed":"2026-09-29","updated":"2026-09-29","orgs":["Google DeepMind","Google Quantum AI"],"title":"AlphaQubit: neural decoder sets accuracy record for quantum error correction on Google's Sycamore","summary":"AlphaQubit (Nature, Nov 2024), a recurrent-transformer decoder for the surface code, made 6% fewer errors than tensor-network decoding and 30% fewer than correlated matching on real Sycamore data at code distances 3 and 5. It is not yet fast enough for real-time use.","key_facts":["Pre-trained on simulated data, fine-tuned on Sycamore experimental data","Distance 3 (17 qubits) and distance 5 (49 qubits)","Caveat: too slow for real-time decoding on superconducting hardware at the time"],"key_numbers":[],"tags":["physics","quantum-computing","error-correction"],"science":{"field":"physics","subfield":"quantum computing / error correction","problem":"Decoding surface-code error syndromes accurately","result":"Most accurate decoder on real quantum hardware data at the time.","open_since":"","ai_system":["AlphaQubit"],"human_role":"Human-designed model","verification":{"key":"peer-review","label":"Peer-reviewed","text":"Peer-reviewed in Nature"},"status":"confirmed","shock":""},"body_md":"## What happened\n\nDeepMind trained a neural network to infer which errors occurred in a quantum processor from noisy stabiliser measurements.\n\n## Why it matters\n\nBetter decoding lowers the overhead of fault-tolerant quantum computing.","disputed":[],"related":[],"people":[],"posts":[],"videos":[],"models":[],"changes":[{"date":"2026-09-29","type":"filed","text":"Created"}],"provenance":{"agents":[{"model":"Claude Opus 5.5","maker":"Anthropic","tool":"Claude Code"}],"filed":"2026-09-29","run":null,"sources_read":null,"updated":"2026-09-29","human_review":null,"version":{"date":"2026-10-04"}},"gaps":[{"model_id":"gpt-6-astra","name":"GPT-6 Astra","cutoff":"2026-04","days_after":null,"in_training_data":true},{"model_id":"claude-opus-5-5","name":"Claude Opus 5.5","cutoff":"2026-06","days_after":null,"in_training_data":true},{"model_id":"gemini-4-hydrogen","name":"Gemini 4 Hydrogen","cutoff":"2026-03","days_after":null,"in_training_data":true},{"model_id":"grok-4-7","name":"Grok 4.7","cutoff":"2026-05","days_after":null,"in_training_data":true}],"short_url":null}