通过量子库计算中的空间复用提高计算能力
Boosting computational power through spatial multiplexing in quantum reservoir computing
摘要：
量子水库计算提供了一个框架，利用量子系统的自然动力学作为计算资源。它可以实现实时信号处理，并解决一般的时间机器学习问题，这需要内存和最近的输入流的非线性映射，使用量子动力学在计算优势区域，其中经典模拟的系统是棘手的。一个核磁共振自旋系综系统是一个现实的候选人，这种物理实现，这是目前在实验室。在本文中，考虑到这些现实的实验约束，实现的框架，我们引入了一个计划，我们称之为空间复用技术，有效地提高了平台的计算能力。这种技术利用了不相交动力学，它起源于由共同输入流并行驱动的多个不同的量子系统。因此，与设计单个大型量子系统以增加计算节点的量子比特数量不同，可以从多个但小的量子系统中制备大量量子比特，这在实验室实验中易于操作。我们数值上证明了该技术的有效性，使用几个基准任务，并定量研究其规格，有效性范围和限制的细节。
英文摘要：
Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a computational resource. It can implement real-time signal processing and solve temporal machine learning problems in general, which requires memory and nonlinear mapping of the recent input stream using the quantum dynamics in computational supremacy region, where the classical simulation of the system is intractable. A nuclear magnetic resonance spin-ensemble system is one of the realistic candidates for such physical implementations, which is currently available in laboratories. In this paper, considering these realistic experimental constraints for implementing the framework, we introduce a scheme, which we call a spatial multiplexing technique, to effectively boost the computational power of the platform. This technique exploits disjoint dynamics, which originate from multiple different quantum systems driven by common input streams in parallel. Accordingly, unlike designing a single large quantum system to increase the number of qubits for computational nodes, it is possible to prepare a huge number of qubits from multiple but small quantum systems, which are operationally easy to handle in laboratory experiments. We numerically demonstrate the effectiveness of the technique using several benchmark tasks and quantitatively investigate its specifications, range of validity, and limitations in detail.
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