The authors define the QA-KS(φ) gate family embedding Toffoli with Hadamard sandwich and CP kickback, provide its exact 8x8 unitary, and demonstrate orthogonality to CCX on q0=1 inputs while agreeing on q0=0.
Fast and accurate AI-based pre-decoders for surface codes
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Fast, scalable decoding architectures that operate in a block-wise parallel fashion across space and time are essential for real-time fault-tolerant quantum computing. We introduce a scalable AI-based pre-decoder for the surface code that performs local, parallel error correction with low decoding runtimes, removing the majority of physical errors before passing residual syndromes to a downstream global decoder. This modular architecture is backend-agnostic and composes with arbitrary global decoding algorithms designed for surface codes, and our implementation is completely open source. Integrated with uncorrelated PyMatching, the pipeline achieves end-to-end decoding runtimes of order $\mathcal{O}(1 \mu\text{s})$ per round at large code distances on NVIDIA GB300 GPUs while reducing logical error rates (LERs) relative to global decoding alone. In a block-wise parallel decoding scheme with access to multiple GPUs, the decoding runtime can be reduced to well below $\mathcal{O}(1 \mu\text{s})$ per round. We observe further LER improvements by training a larger model, outperforming correlated PyMatching up to distance-13. We additionally introduce a noise-learning architecture that infers decoding weights directly from experimentally accessible syndrome statistics without requiring an explicit circuit-level noise model. We show that purely data-driven graph weight estimation can nearly match uncorrelated PyMatching and exceed correlated PyMatching in certain regimes, enabling highly-optimized decoding when hardware noise models are unknown or time-varying, as well as training pre-decoders with realistic noise models. Together, these results establish a practical, modular, and high-throughput decoding framework suitable for large-distance surface-code implementations.
fields
quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
An FPGA-based neural-network decoder achieves 550 ns deterministic closed-loop latency for real-time distance-3 surface code error correction on a superconducting processor, matching offline decoding performance.
A white paper proposing a six-layer system stack for real-time quantum error correction, with benchmarks of decoders for surface and qLDPC codes and latency models.
citing papers explorer
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Quantum-Adaptive KS($\varphi$): A Parameterized Three-Qubit Gate Family Embedding Toffoli with Measurement-Free Phase Kickback and Intrinsic Error Non-Amplification
The authors define the QA-KS(φ) gate family embedding Toffoli with Hadamard sandwich and CP kickback, provide its exact 8x8 unitary, and demonstrate orthogonality to CCX on q0=1 inputs while agreeing on q0=0.
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Real-time Surface-Code Error Correction Using an FPGA-based Neural-Network Decoder
An FPGA-based neural-network decoder achieves 550 ns deterministic closed-loop latency for real-time distance-3 surface code error correction on a superconducting processor, matching offline decoding performance.
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Real-Time Quantum Error Correction System Stack: Architecture, Algorithms, and Engineering Practice
A white paper proposing a six-layer system stack for real-time quantum error correction, with benchmarks of decoders for surface and qLDPC codes and latency models.