标题： 量子回声状态网络在时间序列任务中的记忆重置率优化 显示英文标题

标题： Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks

摘要： 量子 reservoir 计算是一种量子机器学习算法类别，涉及基于量子位寄存器的回声状态网络 reservoir，但其记忆容量对超参数的依赖关系仍然相当不清楚。 为了在时间序列预测任务中最大化其准确性，我们研究了网络记忆与量子 reservoir 演化重置率之间的关系。 我们在 IBM 量子硬件上通过三个具有衰减记忆的非线性映射来评估网络性能。 对于区间 [0,1] 内记忆重置率的中间值，量子 reservoir 的记忆容量达到最大。 如预期的那样，记忆容量随着量子位数量近似线性增加。 在优化记忆重置率后，任务中预测输出的均方误差可能比先前的实现降低约 1/5。 显示英文摘要

摘要： Quantum reservoir computing is a class of quantum machine learning algorithms involving a reservoir of an echo state network based on a register of qubits, but the dependence of its memory capacity on the hyperparameters is still rather unclear. In order to maximize its accuracy in time--series predictive tasks, we investigate the relation between the memory of the network and the reset rate of the evolution of the quantum reservoir. We benchmark the network performance by three non--linear maps with fading memory on IBM quantum hardware. The memory capacity of the quantum reservoir is maximized for central values of the memory reset rate in the interval [0,1]. As expected, the memory capacity increases approximately linearly with the number of qubits. After optimization of the memory reset rate, the mean squared errors of the predicted outputs in the tasks may decrease by a factor ~1/5 with respect to previous implementations.