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- W4317547002 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." @default.
- W4317547002 created "2023-01-20" @default.
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- W4317547002 date "2023-01-17" @default.
- W4317547002 modified "2023-09-29" @default.
- W4317547002 title "Taking advantage of noise in quantum reservoir computing" @default.
- W4317547002 doi "https://doi.org/10.48550/arxiv.2301.06814" @default.
- W4317547002 hasPublicationYear "2023" @default.
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