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13 March 2024 Exploring quantum mechanical advantage for reservoir computing
Christopher Gies, Frederik Lohof, Niclas Götting
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030S (2024) https://doi.org/10.1117/12.3001523
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
The reservoir computing paradigm has proven effective for autonomous learning and time-series prediction. While classical reservoir computers have been extensively studied, quantum counterparts are gaining attention. Quantum reservoir computers (QRCs) offer advantages like exponential phase-space dimension scaling and entanglement as a unique resource. With advancements in semiconductor fabrication techniques for quantum-photonic systems, such as coupled-cavity arrays, QRC realization is imminent. We explore the properties and quantum advantage of QRCs based on the transverse-field Ising model. Using the benchmark of linear short-term memory capacity, we evaluate the QRC's performance in terms of entanglement and covariance dimension. Possible implementations using interconnected nanolasers as a semiconductor-based quantum-photonic neural network are discussed. [Götting et al., arXiv:2302.03595 (2023)].
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Christopher Gies, Frederik Lohof, and Niclas Götting "Exploring quantum mechanical advantage for reservoir computing", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030S (13 March 2024); https://doi.org/10.1117/12.3001523
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KEYWORDS
Quantum computing
Quantum advantages
Reservoir computing
Computing systems
Quantum entanglement
Quantum machine learning
Quantum systems
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