近期量子硬件高精度测量的实用技术及其在分子能量估计中的应用

IF 8.3 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Keijo Korhonen, Hetta Vappula, Adam Glos, Marco Cattaneo, Zoltán Zimborás, Elsi-Mari Borrelli, Matteo A. C. Rossi, Guillermo García-Pérez, Daniel Cavalcanti
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引用次数: 0

摘要

实现近期量子器件的高精度测量对于推进量子计算应用至关重要。量子计算机具有很高的读出误差,使得具有高精度要求的量子模拟特别具有挑战性。本文通过解决关键开销和噪声源实现了实用技术,以达到量子化学必不可少的精度。具体来说,我们利用局部偏置随机测量来减少拍摄开销,使用并行量子探测器断层扫描来减少电路开销和减轻读出错误的重复设置,以及混合调度来减轻时间依赖性噪声。我们通过在IBM Eagle r3上对Hartree-Fock状态下的BODIPY分子进行分子能量估计来演示这些技术,将测量误差从1-5%降低到0.16%。这些策略为更可靠的量子计算铺平了道路,特别是对于需要精确分子能量计算的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Practical techniques for high-precision measurements on near-term quantum hardware and applications in molecular energy estimation

Practical techniques for high-precision measurements on near-term quantum hardware and applications in molecular energy estimation

Achieving high-precision measurements on near-term quantum devices is critical for advancing quantum computing applications. Quantum computers suffer from high readout errors, making quantum simulations with high accuracy requirements particularly challenging. This paper implements practical techniques to reach accuracies essential for quantum chemistry by addressing key overheads and noise sources. Specifically, we leverage locally biased random measurements for reducing shot overhead, repeated settings with parallel quantum detector tomography for reducing circuit overhead and mitigating readout errors, and blended scheduling for mitigating time-dependent noise. We demonstrate these techniques via molecular energy estimation of the BODIPY molecule on a Hartree-Fock state on an IBM Eagle r3, obtaining a reduction in measurement errors by an order of magnitude from 1-5% to 0.16%. These strategies pave the way for more reliable quantum computations, particularly for applications requiring precise molecular energy calculations.

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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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