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, the most promising applications of near-term quantum hardware. Finally, we show that integrating these two features into the Rigetti Quantum Cloud Services 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古典云平台

为了支持量子计算的近期应用，出现了一种新的计算范例-量子经典云，其中量子计算机（QPU）通过共享的云基础架构与经典计算机（CPU）协同工作。在这项工作中，我们列举了量子古典云平台的架构要求，并提出了一个基准测试其运行时性能的框架。此外，我们还将介绍两个平台级别的增强功能，即参数编译和主动qubit重置，它们专门优化了量子古典体系结构，以支持变分混合算法，这是近期量子硬件最有前途的应用。最后，