论文标题
通过量子储层计算实现和压缩量子电路
Realising and compressing quantum circuits with quantum reservoir computing
论文作者
论文摘要
量子计算机需要精确控制参数和仔细的基础物理系统工程。相比之下，神经网络已经发展为忍受不精确和不均匀性。在这里，使用储层计算体系结构，我们展示了如何将量子节点的随机网络用作量子计算的可靠硬件。我们的网络体系结构仅通过优化单层量子节点来诱导量子操作，这比必须优化许多神经元的传统神经网络的关键优势。我们演示了单个网络如何诱导不同的量子门，包括通用门集。此外，在少数Quibent机制中，我们表明量子电路中多个量子门的序列可以通过单个操作来压缩，从而有可能降低操作时间和复杂性。由于关键资源是一个随机的节点网络，没有特定的拓扑或结构，因此该体系结构是用于量子计算的硬件友好型替代范式。
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.
