论文标题
表征Transmon Qubit储存库的内存能力
Characterizing the memory capacity of transmon qubit reservoirs
论文作者
论文摘要
量子储存计算（QRC）利用用于机器学习的量子集合系统的动力学。数值实验表明，由5-7个量子位组成的量子系统具有与100至500个节点的常规复发性神经网络相当的计算能力。与传统的神经网络不同，我们不了解用于高性能信息处理的水库设计的指导原则。了解量子储层的记忆能力仍然是一个空旷的问题。在这项研究中，我们专注于表征使用IBM提供的Transmon设备构建的量子储存库的内存能力的任务。我们的混合储层达到了6x10^{ - 4}的归一化均方根误差（NMSE），与最近的基准相当。 N Qubit储层的记忆能力表征显示出具有拓扑复杂性的系统变化，并表现出与N-1自环构造的峰值。这样的峰为选择预测任务的最佳设计提供了基础。
Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6x10^{-4} which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n-1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.
