Journal Article

FZJ-2023-01404

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Quantum Error Correction with Quantum Autoencoders

Locher, D. (Corresponding author)FZJ* ; Cardarelli, L.FZJ* ; Müller, M. (Corresponding author)FZJ*

2023
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften Wien

This record in other databases:    

Please use a persistent id in citations: http://hdl.handle.net/2128/34362  doi:10.22331/q-2023-03-09-942

Abstract: Active quantum error correction is a central ingredient to achieve robust quantum processors. Inthis 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, includingspatially correlated computational errors as well as qubit losses. We highlight that the denoisingcapabilities of quantum autoencoders are not limited to the protection of specific states but extendto the entire logical codespace. We also show that quantum neural networks can be used to discovernew 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 besuccessfully used to perform beneficial quantum error correction.

---

Contributing Institute(s):

1. Theoretische Nanoelektronik (PGI-2)

Research Program(s):

1. 5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522) (POF4-522)

Appears in the scientific report 2023

---

Database coverage:
; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---