基于变分量子的圆柱波导仿真

IF 1.8 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Emanuel Colella;Benjamin A. Baldwin;Shaun F. Kelso;Luca Bastianelli;Valter Mariani Primiani;Franco Moglie;Gabriele Gradoni
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引用次数: 0

摘要

噪声中尺度量子(NISQ)系统的出现标志着量子计算发展进入了一个重要阶段。尽管由于量子比特数和噪声敏感性有限而受到限制,NISQ设备在通过混合经典量子算法解决复杂计算挑战方面表现出巨大潜力。在各种混合算法中,变分量子算法(VQAs)由于能够解决经典算法无法解决的高度复杂的大规模问题而越来越受到关注。特别是,变分量子特征求解器(VQE)在计算大型系统的能量和基态方面显示出它的潜力,在这些系统中,解决此类问题的复杂性呈指数级增长,对于经典计算机来说变得难以处理。在这方面,本文的目的是扩展VQE在求解圆波导模式中的应用,以验证其在数学上复杂的电磁问题中的适用性。特别地,我们建议计算圆波导中横向电和横向磁情况下的基模和一些高阶模。由于几何的性质和圆形结构的相关边界条件,这在数学上是具有挑战性的。结果证实了将VQE应用于数学上复杂的电磁问题的可能性,并宣布了其扩展和解决经典算法无法解决的高维、大规模电磁问题的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variational Quantum Based Simulation of Cylindrical Waveguides
The advent of noisy intermediate-scale quantum (NISQ) systems signifies an important stage in quantum computing development. Despite the constraints due to their limited qubit numbers and noise susceptibility, NISQ devices exhibit substantial potential to tackle complex computational challenges via hybrid classical-quantum algorithms. Among the various hybrid algorithms, variational quantum algorithms (VQAs) are gaining increasing attention due to their ability to solve highly complex, large-scale problems where classical algorithms fail. In particular, the variational quantum eigensolver (VQE) shows its potential in calculating the energies and ground states of large systems, where the complexity of solving such problems grows exponentially and becomes intractable for classical computers. At this regard, the aim of this paper is to extend the use of VQE for solving circular waveguide modes to verify their applicability to mathematically complex EM problems. In particular, we propose to calculate the fundamental and the some higher order modes for both transverse electric and transverse magnetic cases in circular waveguides. This is mathematically challenging due to the nature of geometry and the associated boundary conditions of circular structures. The results confirm the possibility of applying VQE for mathematically complex EM problems, announcing its potential to scale up and solve high-dimensional, large-scale EM problems where classical algorithms can fail.
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来源期刊
CiteScore
4.30
自引率
0.00%
发文量
27
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