AlphaQubit: neural decoder sets accuracy record for quantum error correction on Google’s Sycamore
Result confirmed
The 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.
Status
- Claim
Result confirmed
- Our reporting
- High confidence
- Verification
- Peer-reviewed
- Importance
- 3 of 5
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
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
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.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 2 primary
Primary
- Learning high-accuracy error decoding for quantum processors (Nature)nature.com, paper
- Google: AlphaQubitblog.google, official
Changes
- Filed