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Quantum Error Correction Fault Tolerance Quantum Machine Learning

Correcting quantum errors one gradient step at a time

arXiv

Authors: Manav Seksaria, Anil Prabhakar

Year

2025

Paper ID

5819

Status

Preprint

Abstract Read

~2 min

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Abstract

In this work, we introduce a general, gradient-based method that optimises codewords for a given noise channel and fixed recovery. We do so by differentiating fidelity and descending on the complex coefficients using finite-difference Wirtinger gradients with soft penalties to promote orthonormalisation. We validate the gradients on symmetry checks (XXX/ZZZ repetition codes) and the [[5, 1, 3]] code, then demonstrate substantial gains under isotropic Pauli noise with Petz recovery: fidelity improves from 0.783 to 0.915 in 100 steps for an isotropic Pauli noise of strength 0.05. The procedure is deterministic, highly parallelisable, and highly scalable.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2025 reference point for readers tracking recent quantum research.
In this work, we introduce a general, gradient-based method that optimises codewords for a given noise channel and fixed recovery.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.18061 [2] arXiv https://arxiv.org/abs/2512.18061 [3] Publisher https://arxiv.org/abs/2512.18061

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