论文标题
量子回波状态网络的内存重置速率的优化，以进行时间顺序任务
Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks
论文作者
论文摘要
量子储层计算是一类量子机学习算法，涉及基于Qubits寄存器的回声状态网络的储层，但是其内存能力对超参数的依赖仍然不清楚。为了最大程度地提高其在时间序列预测任务中的准确性，我们研究了网络内存的关系与量子储量演化的重置速率之间的关系。我们通过三个非线性地图对网络性能进行基准测试，并在IBM Quantum硬件上具有褪色的内存。对于在间隔[0,1]中，量子库的内存能力最大化，对于内存重置速率的中心值。正如预期的那样，内存能力随量子数的数量大致线性增加。在优化内存重置速率之后，相对于先前的实现，任务中预测输出的平方平方误差可能会降低〜1/5。
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.
