Leveraging Quantum Dynamics for Physical Computing Applications

Quantum reservoir computing uses genuine quantum properties to process temporal information. It combines machine learning with physical computing in a novel quantum computing paradigm. Unlike gate-based quantum computing, which requires precise qubit control and decoherence suppression, quantum reservoir computing utilizes the dynamics of disordered quantum networks, with moderate noise even benefiting the reservoir's memory capacity. In this study, we explore how quantum properties affect a system's suitability as a reservoir computer. We investigate the relationship between entanglement in the reservoir and phase space dimension. Our findings show higher entanglement increases the percentage of the exponentially large phase space used for computation. Additionally, we discuss methods to quantify information spread throughout the network. Network symmetries, accidental or intentional, significantly influence dynamics and the reservoir's memory capacity. Lastly, we highlight the potential and limitations of using arrays of semiconductor-based coupled microcavities for hybrid quantum-photonic implementations of reservoir computing.