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)].
Multi-partite entanglement is a key resource for many applications in quantum information technologies. Based on two material platforms, we consider methods for entanglement generation. In quantum-dot molecules [1], electric-field switching is used, and we characterize the separation between adiabatic and diabatic dynamics in the realization of entangled target states. Our numerical simulations are based on the Bloch-Redfield formalism and are a key step towards the realization of fully quantum-mechanical protocol simulation. As a second platform, we demonstrate the realization of multi-partite entangled states in coupled-cavity arrays and discuss their role in novel quantum machine learning concepts like quantum reservoir computing, to which we provide some insight.
[1] Schall et al., Advanced Quantum Technologies 4, 2100002 (2021).
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