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Robust Control Optimization for Quantum Approximate Optimization Algorithms

IFAC-PapersOnLine(2020)

Berkeley Ctr Quantum Informat & Computat | Univ Calif Berkeley | SC Solut

Cited 13|Views40
Abstract
Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.
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quantum approximate optimization algorithm,robust control,sequential convex programming,error mitigation
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要点】:该论文探讨了通过顺序凸规划(SCP)等鲁棒控制优化技术,提高量子近似优化算法(QAOA)在面临已知误差源但误差大小未知的噪声 intermediate scale quantum (NISQ) 设备中的模拟误差的可行性,从而改善了QAOA在存在相干误差和初始状态准备错误时的目标景观和整体电路保真度。

方法】:采用顺序凸规划进行鲁棒控制优化。

实验】:通过实验验证了鲁棒优化技术能显著降低QAOA模拟误差,改善了在特定误差源影响下的QAOA目标函数 landscape和整体电路保真度,实验数据未在文中具体提及。