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arXiv 2301.07445quant-ph
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量子近似优化算法电路中的信息置乱与纠缠

Information scrambling and entanglement in quantum approximate optimization algorithm circuits

Chen Qian, Wei-Feng Zhuang, Rui-Cheng Guo, Meng-Jun Hu, Dong E. Liu

AI总结:

研究量子近似优化算法电路中的信息置乱与纠缠,发现更难的问题通常需要消耗更多量子资源,为未来基准测试量子多体问题复杂度提供了依据。

摘要:

变分量子算法由最优参数化量子电路组成,有望在含噪声中等规模量子(NISQ)时代展示量子优势。除了经典计算资源外,不同种类的量子资源也对计算过程有贡献,例如信息置乱和纠缠。刻画特定问题的复杂度与解决这些问题所消耗的量子资源之间的关系,有助于我们在量子信息处理的背景下理解VQA的结构。在本工作中,我们聚焦于量子近似优化算法(QAOA),旨在解决组合优化问题。我们分别研究了QAOA电路中的信息置乱和纠缠,并发现在大多数情况下,对于更难的问题,QAOA电路需要更多的量子资源来获得解决方案。我们指出,未来我们的结果可用于通过计算过程中的信息置乱或纠缠积累来对量子多体问题的复杂度进行基准测试。

英文摘要:

Variational quantum algorithms, which consist of optimal parameterized quantum circuits, are promising for demonstrating quantum advantages in the noisy intermediate-scale quantum (NISQ) era. Apart from classical computational resources, different kinds of quantum resources have their contributions to the process of computing, such as information scrambling and entanglement. Characterizing the relation between the complexity of specific problems and quantum resources consumed by solving these problems is helpful for us to understand the structure of VQAs in the context of quantum information processing. In this work, we focus on the quantum approximate optimization algorithm (QAOA), which aims to solve combinatorial optimization problems. We study information scrambling and entanglement in QAOA circuits, respectively, and discover that for a harder problem, more quantum resource is required for the QAOA circuit to obtain the solution in most cases. We note that in the future, our results can be used to benchmark the complexity of quantum many-body problems by information scrambling or entanglement accumulation in the computing process.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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