AlphaQubit: neural decoder sets accuracy record for quantum error correction on Google's Sycamore
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
Science result
- Field
- physics / quantum computing / error correction
- Problem
- Decoding surface-code error syndromes accurately
- Result
- Most accurate decoder on real quantum hardware data at the time.
- AI system
- AlphaQubit
- Human role
- Human-designed model
- Verification
- Peer-reviewed in Nature
- Status
- confirmed
What happened
DeepMind trained a neural network to infer which errors occurred in a quantum processor from noisy stabiliser measurements.
Why it matters
Better decoding lowers the overhead of fault-tolerant quantum computing.
Changelog
- 2026-09-29: created
Sources (2)
- paperLearning high-accuracy error decoding for quantum processors (Nature)
- officialGoogle: AlphaQubit
id: 2024-11-20-alphaqubit-quantum-error-correction · updated 2026-09-29 · open in the interactive timeline