ArticleScientific reports2023
Taking advantage of noise in quantum reservoir computing.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- HIV-1 protease cleavage sites detection with a quantum convolutional neural network algorithm.Scientific reports · 2026Article
- Design of a hybrid quantum machine learning architecture and analysis of quantum noise effects.Scientific reports · 2026Article
- Optimal Complexity of Parameterized Quantum Circuits.Entropy (Basel, Switzerland) · 2026Article
- Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction.Scientific reports · 2023Article
- Binding affinity predictions with hybrid quantum-classical convolutional neural networks.Scientific reports · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, we demonstrate that under some circumstances, quantum noise can be used to improve the performance of quantum reservoir computing, a prominent and recent quantum machine learning algorithm. Our results show that the amplitude damping noise can be beneficial to machine learning, while the depolarizing and phase damping noises should be prioritized for correction. This critical result sheds new light into the physical mechanisms underlying quantum devices, providing solid practical prescriptions for a successful implementation of quantum information processing in nowadays hardware.
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Registered trials
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