用量子库计算实现和压缩量子线路
Realising and compressing quantum circuits with quantum reservoir computing
摘要：
量子计算机需要对参数进行精确控制，并对底层物理系统进行精心设计。相比之下，神经网络已经进化到可以容忍不精确和不均匀。在这里，使用水库计算架构，我们展示了量子节点的随机网络如何可以用作量子计算的鲁棒硬件。我们的网络架构通过仅优化单层量子节点来诱导量子操作，这是传统神经网络的一个关键优势，因为传统神经网络必须优化多层神经元。我们演示了如何一个单一的网络可以诱导不同的量子门，包括一个通用的门集。此外，在几个量子比特的制度，我们表明，在量子线路中的多个量子门的序列可以被压缩与一个单一的操作，潜在地减少操作时间和复杂度。由于关键资源是节点的随机网络，没有特定的拓扑或结构，因此该架构是量子计算的硬件友好的替代范例。
英文摘要：
Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. Our network architecture induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, in the few-qubit regime, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, potentially reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
