论文标题
通过优化的两个Quibit非选择性测量连接的两个量子储层计算
Computing with two quantum reservoirs connected via optimized two-qubit nonselective measurements
论文作者
论文摘要
当前，量子储层计算是用于混合量子，古典机器学习的最有前途且实验可访问的技术之一。但是，由于对量子系统大小和噪声影响的实际限制，其应用受到限制。在这里，我们提出了一种新的方法，可以连接网络中的两个量子储层，以克服这些问题并增强其计算性能。为了在量子储层之间传输信息，我们执行了优化的两Q Q Q Q Q Q Q Q Q Q Q量的非选择性测量。我们建议一种基于张量网络语言的一般启发式优化策略和指定用于量子储层计算的两量价量子通道的矩阵表示。此外，我们引入了一个量子净化通道及其优化，以进一步增强量子储层计算。我们还证明，应用于七个Qubit网络的优化渠道可以在其部分之间有效地传输信息，从而可以通过经典信息链接连接的二十五个Qubits的网络可相当。
Currently, quantum reservoir computing is one of the most promising and experimentally accessible techniques for hybrid, quantum-classical machine learning. However, its applications are limited due to practical restrictions on the size of quantum systems and the influence of noise. Here we propose a novel approach to connect two quantum reservoirs in a network to overcome these issues and enhance their computing performance. To transfer information between quantum reservoirs, we perform optimized two-qubit non-selective measurements. We suggest a general heuristic optimization strategy based on tensor network language and matrix representation of two-qubit quantum channels specified for quantum reservoir computing. In addition, we introduce a single qubit purification channel and its optimization for further enhancement of quantum reservoir computing. We also demonstrate that the optimized channels applied to a seven-qubit network can efficiently transfer information between its parts with the resulting performance comparable to a network up to twenty-five qubits connected via a classical information link.
