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引用次数: 0
摘要
本文介绍了通过协同优化门序列、算法参数和量子位映射来提高数字化逆绝热量子优化算法性能的策略。利用算法和硬件优势,在实际硬件上的演示验证了这些策略的有效性。具体地说,与 IBM 量子处理器上的 Qiskit 和 Tket 相比,我们的方法在没有错误缓解的情况下,近似率平均提高了 4.49 倍;在有错误缓解的情况下,近似率平均提高了 84.8%。这些发现为算法实现的代码设计提供了宝贵的见解,为优化量子比特映射和算法参数量身定制,对提高近期量子设备上的算法性能具有更广泛的意义。
Improving the performance of digitized counterdiabatic quantum optimization via algorithm-oriented qubit mapping
This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms by cooptimizing gate sequences, algorithm parameters, and qubit mapping. Demonstrations on real hardware validate the effectiveness of these strategies, leveraging both algorithmic and hardware advantages. Specifically, our approach achieves an average increase in approximation ratio of without error mitigation and 84.8% with error mitigation compared to Qiskit and Tket on IBM quantum processors. These findings provide valuable insights into the codesign of algorithm implementation, tailored to optimize qubit mapping and algorithm parameters, with broader implications for enhancing algorithm performance on near-term quantum devices.
期刊介绍:
Physical Review A (PRA) publishes important developments in the rapidly evolving areas of atomic, molecular, and optical (AMO) physics, quantum information, and related fundamental concepts.
PRA covers atomic, molecular, and optical physics, foundations of quantum mechanics, and quantum information, including:
-Fundamental concepts
-Quantum information
-Atomic and molecular structure and dynamics; high-precision measurement
-Atomic and molecular collisions and interactions
-Atomic and molecular processes in external fields, including interactions with strong fields and short pulses
-Matter waves and collective properties of cold atoms and molecules
-Quantum optics, physics of lasers, nonlinear optics, and classical optics