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Quantum reservoir computing for time series prediction

Shiekh, Kylee N.
Kapit, Eliot
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2023-04
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Abstract
Quantum reservoir computing (QRC) is an innovative framework for processing sequential data that has numerous potential advantages over traditional approaches. Unlike conventional neural networks, QRC employs a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis, resulting in fast learning, low training costs, and a variety of hardware implementations. The unique feature of QRC is that instead of back-propagation, data structures are passed through non-linear components of the reservoir, resulting in a reduced number of computations. QRC has been shown to outperform classical neural networks for large data sets and is efficient in modeling atomic system dynamics, producing highly accurate representations of complex quantum dynamics. One of the applications of QRC is in time series prediction, which includes finance, weather prediction, and other fields. With QRC, it is possible to develop highly accurate models that can accurately predict future values based on past observations. QRC can handle large, complex data sets and can adapt to changing conditions over time, making it an ideal tool for time series analysis. By using QRC for time series prediction, it is possible to obtain insights into complex phenomena that would be difficult or impossible to obtain using traditional methods. Overall, QRC represents a powerful new approach to sequential data processing that has the potential to revolutionize a wide range of fields.
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