论文标题
针对各种混合算法优化的量子古典云平台
A quantum-classical cloud platform optimized for variational hybrid algorithms
论文作者
论文摘要
为了支持量子计算的近期应用，新的计算范式已经出现了 - 量子计算机（QPU）通过共享云基础架构与经典计算机（CPU）一起起作用。在这项工作中，我们列举了量子古典云平台的建筑要求，并提出了一个基准测试其运行时性能的框架。此外，我们浏览了两个平台级增强功能，参数汇编和主动量子重置，它们专门优化了量子古典体系结构，以支持变异混合算法（VHAS），这是近端量子硬件的最有希望的应用。最后，我们表明，将这两个功能集成到Rigetti量子云服务（QCS）平台中，可以对控制算法运行时的潜伏期进行大量改进。
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.
