DOE Genesis Mission award
With UC San Diego and Berkeley Lab: a $740K+ project to advance AI-accelerated quantum computing — selected from more than 5,000 applications, 278 funded (<6%).
DOE announcement →The AI-native infrastructure layer for scalable quantum computing. We turn quantum data into the AI advantage that scales quantum computing.
Reaching roughly 10,000 physical qubits unlocks the first practically useful applications. Every hardware modality — superconducting, trapped ion, neutral atom — faces the same wall: quantum error correction.
Trained on real quantum data, our decoder reaches a lower logical error rate, faster, than rule-based algorithms — while staying small enough to run inside the control loop.
We build on qLDPC codes: one code block can carry hundreds of logical qubits, where traditional QEC yields a few or even one.
Useful computation needs it below 10-11. That threshold is where rule-based decoders run out of road — and where learning takes over.
Published results already show AI-native decoding ahead by up to two orders of magnitude on key control tasks.
Research models use millions to billions of parameters — too large for real-time hardware. We compress to below 10K parameters and run on FPGA.
Quantum begins with a data-generation problem. Each experiment produces control and error data; aggregated across modalities, it trains better models, which in turn improve the systems that produce more data.
Every experiment creates new control and error data.
Knowledge combines across modalities — what no single hardware player can see alone.
More diverse data produces better control, calibration and correction.
Better performance drives more deployments — and more data.
Additional partnerships are confidential and are not listed here.
With UC San Diego and Berkeley Lab: a $740K+ project to advance AI-accelerated quantum computing — selected from more than 5,000 applications, 278 funded (<6%).
DOE announcement →Error detection benchmarked on IBM superconducting hardware at up to 74 physical qubits, mapping the practical pseudothreshold for near-term devices.
arXiv:2605.02861 →An attention-based decoder approaching the theoretically optimal error threshold on surface codes — and the first neural scaling laws in quantum error correction. Open source.
GitHub →AI will guide the quantum breakthrough. We are building the intelligence and control layer that every useful quantum computer will need — whichever hardware path wins first.
We work with hardware teams, research groups and infrastructure partners.