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QI: Fachverband Quanteninformation
QI 2: Quantum Computing and Algorithms I
QI 2.5: Vortrag
Montag, 20. September 2021, 11:45–12:00, H5
Quantum Autoencoders for Error Correction — •David Locher1, Lorenzo Cardarelli2, and Markus Müller1,2 — 1Institute for Quantum Information, RWTH Aachen University, D-52056 Aachen, Germany — 2Peter Grünberg Institute, Theoretical Nanoelectronics, Forschungszentrum Jülich, D-52425 Jülich, Germany
The operation of reliable large-scale quantum computers will foreseeably require quantum error correction procedures, in order to cope with errors that dynamically occur during storage and processing of fragile quantum information. Classical machine learning approaches, e.g. neural networks, have been proposed and successfully used for flexible and scalable strategies for quantum error correction. Complementary to these efforts, we investigate the potential of quantum machine learning for quantum error correction purposes. Specifically, we show 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 possibly correlated bit-flip and depolarizing noise, as well as qubit loss. We highlight that the denoising possibilities of quantum autoencoders are not limited to the protection of specific states but extend to entire logical codespaces. In addition, we show that quantum neural networks can discover new encodings, optimally adapted to the underlying noise.