Learning from Physics Experiments with Quantum Computers: Applications in Muon Spectroscopy

Sam McArdle
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引用次数: 2

Abstract

Computational physics is an important tool for analysing, verifying, and -- at times -- replacing physical experiments. Nevertheless, simulating quantum systems and analysing quantum data has so far resisted an efficient classical treatment in full generality. While programmable quantum systems have been developed to address this challenge, the resources required for classically intractable problems still lie beyond our reach. In this work, we consider a new target for quantum simulation algorithms; analysing the data arising from physics experiments -- specifically, muon spectroscopy experiments. These experiments can be used to probe the quantum interactions present in condensed matter systems. However, fully analysing their results can require classical computational resources scaling exponentially with the simulated system size, which can limit our understanding of the studied system. We show that this task may be a natural fit for the coming generations of quantum computers. We use classical emulations of our quantum algorithm on systems of up to 29 qubits to analyse real experimental data, and to estimate both the near-term and error corrected resources required for our proposal. We find that our algorithm exhibits good noise resilience, stemming from our desire to extract global parameters from a fitted curve, rather than targeting any individual data point. In some respects, our resource estimates go further than some prior work in quantum simulation, by estimating the resources required to solve a complete task, rather than just to run a given circuit. Taking the overhead of observable measurement and calculating multiple datapoints into account, we find that significant challenges still remain if our algorithm is to become practical for analysing muon spectroscopy data.
从量子计算机的物理实验中学习:在μ介子光谱中的应用
计算物理是分析、验证和(有时)替代物理实验的重要工具。然而,到目前为止,模拟量子系统和分析量子数据还没有得到有效的经典处理。虽然可编程量子系统已经被开发出来应对这一挑战,但解决经典棘手问题所需的资源仍然超出了我们的能力范围。在这项工作中,我们考虑了量子模拟算法的新目标;分析物理实验产生的数据,特别是介子光谱实验。这些实验可以用来探测存在于凝聚态系统中的量子相互作用。然而,充分分析他们的结果可能需要经典的计算资源与模拟系统的大小成指数比例,这可能会限制我们对所研究系统的理解。我们表明,这项任务可能是未来几代量子计算机的自然选择。我们在多达29个量子比特的系统上使用我们的量子算法的经典模拟来分析真实的实验数据,并估计我们的提议所需的近期和纠错资源。我们发现我们的算法表现出良好的噪声弹性,源于我们希望从拟合曲线中提取全局参数,而不是针对任何单个数据点。在某些方面,我们的资源估计比量子模拟中的一些先前工作走得更远,通过估计解决完整任务所需的资源,而不仅仅是运行给定的电路。考虑到观测测量和计算多个数据点的开销,我们发现如果我们的算法要成为分析μ子光谱数据的实用方法,仍然存在重大挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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