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arXiv 2602.04740quant-ph

高效资源的数字化绝热量子分解

Resource-Efficient Digitized Adiabatic Quantum Factorization

Felip Pellicer, Juan José García-Ripoll, Alan C. Santos

AI总结:

本文提出了一种基于核子空间编码的数字化绝热量子分解方法,通过QUBO方法实现更高效的整数分解。

摘要:

数字化绝热量子分解是一种混合算法,利用数字化量子计算机的优势,通过门分解模拟连续演化,实现高效的分解绝热算法。在本文中,我们利用数字化计算机的灵活性,推导出一种能够降低实现分解所需门操作成本的数字化绝热算法。为此,我们提出了一种新的绝热分解方法,通过将问题的解编码到问题哈密顿量的核子空间中,而不是像Peng等人[Phys. Rev. Lett. 101, 220405 (2008)]提出的标准绝热分解中所使用的基态编码。我们的编码使能够设计属于二次无约束二进制优化(QUBO)方法类别的绝热分解算法,而不是标准绝热分解所使用的多项式无约束二进制优化(PUBO)方法。我们通过实现对整数N(最多8位)的分解来展示我们QUBO算法的性能。结果表明,与PUBO方法相比,在电路复杂度降低和正确解识别的保真度提高方面有显著改进。

英文摘要:

Digitized adiabatic quantum factorization is a hybrid algorithm that exploits the advantage of digitized quantum computers to implement efficient adiabatic algorithms for factorization through gate decompositions of analog evolutions. In this paper, we harness the flexibility of digitized computers to derive a digitized adiabatic algorithm able to reduce the gate-demanding costs of implementing factorization. To this end, we propose a new approach for adiabatic factorization by encoding the solution of the problem in the kernel subspace of the problem Hamiltonian, instead of using ground-state encoding considered in the standard adiabatic factorization proposed by Peng $et$ $al$. [Phys. Rev. Lett. 101, 220405 (2008)]. Our encoding enables the design of adiabatic factorization algorithms belonging to the class of Quadratic Unconstrained Binary Optimization (QUBO) methods, instead the Polinomial Unconstrained Binary Optimization (PUBO) used by standard adiabatic factorization. We illustrate the performance of our QUBO algorithm by implementing the factorization of integers $N$ up to 8 bits. The results demonstrate a substantial improvement over the PUBO formulation, both in terms of reduced circuit complexity and increased fidelity in identifying the correct solution.

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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