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This is a theory paper on decoding for approximate quantum error correction. It shows that the Petz recovery map, a near-optimal but normally awkward decoder, can be implemented with a single quantum singular value transformation, with an error bound that shrinks exponentially in the number of iterations. For quantum computing readers, the practical value is a more circuit-friendly recovery method and a clearer framework that unifies existing scrambling decoders, though the results remain analytical and have not been demonstrated on hardware.
Sources coveringarXiv, LessWrong, AI Alignment Forum, Quantum Zeitgeist
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Petz Recovery through a Single Polar Transformation for Approximate Quantum Error Correction

arXiv2 min read

arXiv:2610.20515v1 Announce Type: new Abstract: The Petz recovery map provides a near optimal decoder for approximate quantum error correction, but its inverse square-root normalization complicates circuit implementation. We use the polar structure of the Petz recovery isometry to implement recovery through a single quantum singular value transformation (QSVT). We give explicit block-encoding circuits and an extended recovery isometry that yields approximate Petz recovery without postselection. We bound the diamond distance between the approximate and ideal Petz encoding-recovery channels in terms of a spectrum-weighted loss, which provides a channel specific objective for optimizing the QSVT angles. Uniform polynomial approximation yields an asymptotic error bound that decays exponentially with the number of QSVT iterations, at a rate controlled by the smallest nonzero scaled singular value. Applications to Haar-random encoding and the Hayden-Preskill protocol illustrate the tradeoff between circuit depth and recovery accuracy and place existing scrambling decoders within a common Petz recovery framework.

Abstract:The Petz recovery map provides a near optimal decoder for approximate quantum error correction, but its inverse square-root normalization complicates circuit implementation. We use the polar structure of the Petz recovery isometry to implement recovery through a single quantum singular value transformation (QSVT). We give explicit block-encoding circuits and an extended recovery isometry that yields approximate Petz recovery without postselection. We bound the diamond distance between the approximate and ideal Petz encoding-recovery channels in terms of a spectrum-weighted loss, which provides a channel specific objective for optimizing the QSVT angles. Uniform polynomial approximation yields an asymptotic error bound that decays exponentially with the number of QSVT iterations, at a rate controlled by the smallest nonzero scaled singular value. Applications to Haar-random encoding and the Hayden-Preskill protocol illustrate the tradeoff between circuit depth and recovery accuracy and place existing scrambling decoders within a common Petz recovery framework.

arXiv:2610.20515 [quant-ph]

(or arXiv:2610.20515v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.20515 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 237 words

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