Error mitigation in brainbox quantum autoencoders

Quantum hardware faces noise challenges that disrupt multiqubit entangled states. Quantum autoencoder circuits with a single qubit bottleneck have demonstrated the capability to correct errors in noisy entangled states. By introducing slightly more complex structures in the bottleneck, referred to a...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 2257 - 12
Hauptverfasser: Pazem, Joséphine, Ansari, Mohammad H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 17.01.2025
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Automation
Data compression
Entropy
Error correction & detection
Humanities and Social Sciences
Localization
Machine learning
multidisciplinary
Neural networks
Phase transitions
Quantum computing
Science
Science (multidisciplinary)
Success
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Quantum hardware faces noise challenges that disrupt multiqubit entangled states. Quantum autoencoder circuits with a single qubit bottleneck have demonstrated the capability to correct errors in noisy entangled states. By introducing slightly more complex structures in the bottleneck, referred to as brainboxes, the denoising process can occure more quickly and efficiently in the presence of stronger noise channels. Selecting the most suitable brainbox for the bottleneck involves a trade-off between the intensity of noise on the hardware and training complexity. Finally, by analysing the Rényi entropy flow throughout the networks, we demonstrate that the localization of entanglement plays a central role in denoising through learning.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-84171-z