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Paper 1

Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning

Yariv Yanay

Year

2026

Journal

arXiv preprint

DOI

arXiv:2601.08014

arXiv

2601.08014

Quantum error correction is one of the fundamental building blocks of digital quantum computation. The Quantum Lego formalism has introduced a systematic way of constructing new stabilizer codes out of basic lego-like building blocks, which in previous work we have used to generate improved error correcting codes via an automated reinforcement learning process. Here, we take this a step further and show the use of a hybrid classical-quantum algorithm. We combine classical reinforcement learning with calls to two commercial quantum devices to search for a stabilizer code to correct errors specific to the device, as well as an induced photon loss error.

Paper 2

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