Место издания:Prokhorov General Physics Institute of the Russian Academy of Sciences Москва
Первая страница:67
Последняя страница:67
Аннотация:A quantum memristor is a quantum system with a dynamic internal state and hysteretic input-output characteristics, capable of performing operations with quantum information [1]. We studya three-level quantum memristor implemented on a single ultracold 171Yb ion confined in a Paultrap, where the input and output signals are determined by the populations of the energy levels,and the system is controlled by two resonant laser pulses with Gaussian envelopes [2].We focus on the experimentally relevant regime where the two resonant pulses are significantlyseparated in time, so that during each pulse the other field envelope is negligible. Under theseconditions, we derive explicit closed-form analytical expressions for the population dynamics andthe memristor's input (x) and output (y) signals. The resulting dependences are expressed via theerror-function integrals of Gaussian envelopes). The obtained analytical dependences for x and yare benchmarked against direct numerical integration of the original equations and show goodagreement; for representative parameters used in the paper the reported relative standard deviationis below 0.03%.Within the memristor formalism, we introduce a control parameter (denoted as the reflection-coefficient analog R) and a feedback model with a sliding integration window,similar to photonicplatform [3]. We demonstrate that the output-input hysteresis y(x) exhibits strong dependence onthe window parameter T, which is essential for tuning the memristor response and implementingneuromorphic computing devices on the ion platform. We also compare the separated-pulsesregime to the previously studied simultaneous-pulses configuration and observe that hysteresispersists, while the output variation range can change [4].The developed analytical approach provides a consistent method for determining input andoutput signals, simplifying both experimental verification and the analytical simulation of ion-based quantum memristors for neuromorphic applications.This work was supported by the Russian Science Foundation (project no. 24-12-00415).