In a dissipative qubit network used as a quantum reservoir, the dissipation strength that maximizes short-term memory also maximizes resonant optical absorption.
Quantum Reservoir Computing Using Bose-Einstein Condensate with Damping
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Quantum reservoir computing is a type of machine learning in which the high-dimensional Hilbert space of quantum systems contributes to performance. In this study, we employ the Bose-Einstein condensate of dilute atomic gas as a reservoir to examine the effect of reduction in the number of condensed particles, damping, and the nonlinearity of the dynamics. It is observed that for the condensate to function as a reservoir, the physical system requires damping. The nonlinearity of the dynamics improves the performance of the reservoir, while the reduction in the number of condensed particles degrades the performance.
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Connection between memory performance and optical absorption in quantum reservoir computing
In a dissipative qubit network used as a quantum reservoir, the dissipation strength that maximizes short-term memory also maximizes resonant optical absorption.