Frequency- and dissipation-dependent entanglement advantage in spin-network quantum reservoir computing
Youssef Kora, Hadi Zadeh‐Haghighi, Terrence C. Stewart, Khabat Heshami, Christoph Simon · Physical Review A · 2024
We study the performance of an Ising spin network for quantum reservoir computing in linear and nonlinear memory tasks. We investigate the extent to which quantumness enhances performance by monitoring the behavior of quantum entanglement, which we quantify by the partial transpose of the density matrix. In the most general case where the effects of dissipation are incorporated, our results indicate that the strength of the entanglement advantage depends on the frequency of the input signal; the benefit of entanglement is greater with more rapidly fluctuating signals, whereas a low-frequency input is better suited to a nonentangled reservoir. This may be understood as a condition for an entanglement advantage to manifest itself: the system's quantum memory must survive long enough for the temporal structure of the input signal to reveal itself. We also find that quantum entanglement empowers a spin-network quantum reservoir to remember a greater number of temporal features.