Quantum Error Correction with Quantum Autoencoders

• URL: http://arxiv.org/abs/2202.00555v1
• Date: Tue, 1 Feb 2022 16:55:14 GMT
• Title: Quantum Error Correction with Quantum Autoencoders
• Authors: David F. Locher, Lorenzo Cardarelli, Markus M\"uller
• Abstract summary: We show how quantum neural networks can be trained to learn optimal strategies for active detection and correction of errors. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Active quantum error correction is a central ingredient to achieve robust quantum processors. In this paper we investigate the potential of quantum machine learning for quantum error correction. Specifically, we demonstrate how quantum neural networks, in the form of quantum autoencoders, can be trained to learn optimal strategies for active detection and correction of errors, including spatially correlated computational errors as well as qubit losses. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace. We also show that quantum neural networks can be used to discover new logical encodings that are optimally adapted to the underlying noise. Moreover, we find that, even in the presence of moderate noise in the quantum autoencoders themselves, they may still be successfully used to perform beneficial quantum error correction.

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