In order to support near-term applications of quantum computing, a new compute paradigm has emerged--the quantum-classical cloud--in which quantum computers (QPUs) work in tandem with classical computers (CPUs) via a shared cloud infrastructure. In this work, we enumerate the architectural requirements of a quantum-classical cloud platform, and present a framework for benchmarking its runtime performance. In addition, we walk through two platform-level enhancements, parametric compilation and active qubit reset, that specifically optimize a quantum-classical architecture to support variational hybrid algorithms (VHAs), the most promising applications of near-term quantum hardware. Finally, we show that integrating these two features into the Rigetti Quantum Cloud Services (QCS) platform results in considerable improvements to the latencies that govern algorithm runtime.

中文翻译：

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针对变分混合算法优化的量子经典云平台

为了支持量子计算的近期应用，出现了一种新的计算范式——量子经典云——其中量子计算机 (QPU) 通过共享云基础设施与经典计算机 (CPU) 协同工作。在这项工作中，我们列举了量子经典云平台的架构要求，并提出了一个用于对其运行时性能进行基准测试的框架。此外，我们还介绍了两个平台级增强功能，参数编译和主动量子位重置，它们专门优化了量子经典架构以支持变分混合算法 (VHA)，这是近期量子硬件最有前途的应用。最后，