Parallel circuit implementation of variational quantum algorithms

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Michele Cattelan, Sheir Yarkoni, Wolfgang Lechner
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引用次数: 0

Abstract

We present a framework to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution to solve problems larger than the number of available qubits in a quantum device. We apply this method to combinatorial optimization problems, where inherent structures can be identified, and show how to implement these parallelized quantum circuits. We show how to formulate an objective function for the classical optimizer to guide the optimization towards meaningful solutions. We test our framework by creating a parallelized version of the Quantum Approximate Optimization Algorithm and a variational version of quantum annealing and explain how our framework applies to other quantum optimization algorithms. We provide results obtained both from simulation and experiments on real hardware. Our results show that the information lost by splitting the quantum circuits can be partially recovered by optimizing a global objective function evaluated with the separate circuit samples.

Abstract Image

变分量子算法的并行电路实现
我们提出了一个框架来分割变分量子算法(VQAs)的量子电路,以允许并行训练和执行,以解决比量子设备中可用量子比特数量更大的问题。我们将此方法应用于组合优化问题,其中固有结构可以被识别,并展示如何实现这些并行量子电路。我们展示了如何为经典优化器制定一个目标函数,以指导优化朝着有意义的解决方案。我们通过创建量子近似优化算法的并行版本和量子退火的变分版本来测试我们的框架,并解释我们的框架如何应用于其他量子优化算法。我们提供了仿真和实际硬件实验的结果。我们的研究结果表明,通过优化用单独的电路样本评估的全局目标函数,可以部分恢复因分裂量子电路而丢失的信息。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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