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Quantum Error Correction with Quantum Autoencoders
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AI
DOI：10.22331/q-2023-03-09-942.png)
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Abstract
En 中文
Active quantum error correction is a central ingredient to achieve robust quantum proces-sors. In this paper we investigate the potential of quantum machine learning for quantum er-ror correction in a quantum memory. Specif-ically, we demonstrate how quantum neural networks, in the form of quantum autoen-coders, can be trained to learn optimal strate-gies for active detection and correction of er-rors, including spatially correlated computa-tional errors as well as qubit losses. We high-light that the denoising capabilities of quan-tum autoencoders are not limited to the pro-tection of specific states but extend to the entire logical codespace. We also show that quantum neural networks can be used to dis-cover new logical encodings that are optimally adapted to the underlying noise. Moreover, we find that, even in the presence of moder-ate noise in the quantum autoencoders them-selves, they may still be successfully used to perform beneficial quantum error correction and thereby extend the lifetime of a logical qubit.
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