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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).
Gies et al. (Tue,) studied this question.