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arXiv 2602.04719quant-ph
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通过超导量子比特实现大规模数字量子模拟

Enabling large-scale digital quantum simulations with superconducting qubits

Faculté des sciences et techniques de l’ingénieur(理工学院);Laboratoire de théorie et simulation des matériaux(材料理论与模拟实验室)

Laurin E. Fischer

AI总结:

本研究通过探索硬件创新、噪声建模和算法改进,提升超导量子比特在大规模数字量子模拟中的性能。

摘要:

量子计算有潜力通过全新的信息处理方式革新多个科学和技术领域。其中最引人注目的应用是数字量子模拟,即使用量子计算机来模拟其他量子系统的行为。这可能使研究那些在经典计算机上无法解决的问题,从而变革量子化学、凝聚态物理和材料科学等领域。尽管有这种潜力,但实际实现相关问题的实用量子优势却受到当前设备缺陷的阻碍。这同样影响到基于超导电路的量子硬件,这是最先进且可扩展的平台之一。设想的长期解决方案是容错量子计算机,能够自行纠正错误,但主要由于相关的量子比特数量开销而遥不可及。因此,该领域已发展出结合量子和经典资源、利用硬件原生操作以及采用误差缓解技术来从噪声数据中提取有意义结果的策略。本博士论文致力于此更广泛的努力,通过探索在完整计算栈上推进量子模拟的方法,包括硬件层面的创新、改进的噪声建模和误差缓解技术以及由高效测量处理带来的算法改进。

英文摘要:

Quantum computing promises to revolutionize several scientific and technological domains through fundamentally new ways of processing information. Among its most compelling applications is digital quantum simulation, where quantum computers are used to replicate the behavior of other quantum systems. This could enable the study of problems that are otherwise intractable on classical computers, transforming fields such as quantum chemistry, condensed matter physics, and materials science. Despite this potential, realizations of practical quantum advantage for relevant problems are hindered by imperfections of current devices. This also affects quantum hardware based on superconducting circuits which is among the most advanced and scalable platforms. The envisaged long-term solution of fault-tolerant quantum computers that correct their own errors remains out of reach mainly due to the associated qubit number overhead. As a result, the field has developed strategies that combine quantum and classical resources, exploit hardware-native operations, and employ error mitigation techniques to extract meaningful results from noisy data. This doctoral thesis contributes to this broader effort by exploring methods for advancing quantum simulation across the full computational stack, including hardware-level innovations, refined techniques for noise modeling and error mitigation, and algorithmic improvements enabled by efficient measurement processing.

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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