Variational quantum algorithm for experimental photonic multiparameter estimation

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED npj Quantum Information Pub Date : 2024-02-28 DOI:10.1038/s41534-024-00821-0
Valeria Cimini, Mauro Valeri, Simone Piacentini, Francesco Ceccarelli, Giacomo Corrielli, Roberto Osellame, Nicolò Spagnolo, Fabio Sciarrino
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Abstract

Variational quantum metrology represents a powerful tool to optimize estimation strategies, resulting particularly beneficial for multiparameter estimation problems that often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, we develop a variational approach able to efficiently optimize a quantum multiphase sensor. Leveraging the reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop able to enhance the estimation performances. The quantum circuit evaluations are used to compute the system partial derivatives by applying the parameter-shift rule, and thus reconstruct experimentally the Fisher information matrix. This in turn is adopted as the cost function of a classical learning algorithm run to optimize the measurement settings. Our experimental results showcase significant improvements in estimation accuracy and noise robustness, highlighting the potential of variational techniques for practical applications in quantum sensing and more generally in quantum information processing using photonic circuits.

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用于光子多参数估计实验的变量量子算法
变分量子计量学是优化估算策略的强大工具,尤其适用于多参数估计问题,而这些问题往往受到维度诅咒和计算复杂性的限制。为了克服这些挑战,我们开发了一种变分方法,能够有效优化量子多相传感器。利用集成光子设备的可重构性,我们实现了一个混合量子-经典反馈回路,能够提高估算性能。量子电路评估通过应用参数转移规则来计算系统偏导数,从而通过实验重建费雪信息矩阵。这反过来又被用作经典学习算法的成本函数,以优化测量设置。我们的实验结果表明,估计精度和噪声鲁棒性都有了显著提高,凸显了变分技术在量子传感实际应用中的潜力,以及在使用光子电路进行量子信息处理方面的潜力。
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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.
期刊最新文献
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