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1. Verfasser: Yanay, Yariv
Format: Preprint
Veröffentlicht: 2026
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Online-Zugang:https://arxiv.org/abs/2601.08014
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author Yanay, Yariv
author_facet Yanay, Yariv
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_08014
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning
Yanay, Yariv
Quantum Physics
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.
title Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning
topic Quantum Physics
url https://arxiv.org/abs/2601.08014