Quantum Error Correction with Quantum Autoencoders

arXiv (Cornell University)

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 in a quantum memory. 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 and thereby extend the lifetime of a logical qubit.

Authors 2

1. David F. Locher Aachen Institute for Quantum Information

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Institute for Quantum Information , RWTH Aachen University , D-52056 Aachen , Germany

   Peter Grünberg Institute , Theoretical Nanoelectronics , Forschungszentrum Jülich , D-52425 Jülich , Germany

2. Markus Müller Aachen Institute for Quantum Information

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Institute for Quantum Information , RWTH Aachen University , D-52056 Aachen , Germany

   Peter Grünberg Institute , Theoretical Nanoelectronics , Forschungszentrum Jülich , D-52425 Jülich , Germany