Learning to decode logical circuits

• URL: http://arxiv.org/abs/2504.16999v1
• Date: Wed, 23 Apr 2025 18:00:04 GMT
• Title: Learning to decode logical circuits
• Authors: Yiqing Zhou, Chao Wan, Yichen Xu, Jin Peng Zhou, Kilian Q. Weinberger, Eun-Ah Kim,
• Abstract summary: We introduce a data-centric modular decoder framework, Multi-Core Circuit Decoder (MCCD)<n> MCCD handles both single-qubit and entangling gates within a unified framework.<n>Our approach represents a noise-model solution to the decoding challenge for deep logical quantum circuits.
• Score: 26.510386591426112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of quantum hardware bringing the error-corrected quantum circuits to the near future, the lack of an efficient polynomial-time decoding algorithms for logical circuits presents a critical bottleneck. While quantum memory decoding has been well-studied, inevitable correlated errors introduced by entangling logical gates prevent the straightforward generalization of quantum memory decoders. We introduce a data-centric modular decoder framework, Multi-Core Circuit Decoder (MCCD), consisting of decoder modules corresponding to each logical operation supported by the quantum hardware. The MCCD handles both single-qubit and entangling gates within a unified framework. We train MCCD using mirror-symmetric random Clifford circuits, demonstrating its ability to effectively learn correlated decoding patterns. Through extensive testing on circuits significantly deeper than those used in training, we show that MCCD maintains high logical accuracy while exhibiting competitive polynomial decoding time across increasing circuit depths and code distances. When compared with conventional decoders like Minimum Weight Perfect Matching (MWPM), Most Likely Error (MLE), and Belief Propagation with Ordered Statistics Post-processing (BP-OSD), MCCD achieves competitive accuracy with substantially better time efficiency, particularly for circuits with entangling gates. Our approach represents a noise-model agnostic solution to the decoding challenge for deep logical quantum circuits.

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