Akram Youssry, Yang Yang, Robert J. Chapman, Ben Haylock, Francesco Lenzini, Mirko Lobino, Alberto Peruzzo
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引用次数: 0
摘要
了解和控制工程量子系统是开发实用量子技术的关键。然而,鉴于目前的技术限制,如制造缺陷和环境噪声,这并不总是可能的。为了解决这些问题,人们开发了大量用于量子系统识别和控制的理论和数值方法。这些方法包括传统的曲线拟合(受限于描述系统的模型的准确性)和机器学习(ML)方法(提供高效的控制解决方案,但无法控制模型输出以外的内容,也无法深入了解底层物理过程)。在这里,我们通过实验展示了一种 "灰盒 "方法,即构建量子系统的物理模型,并利用该模型设计最优控制。我们报告了比模型拟合更优越的性能,同时还生成了单元和哈密顿,这些都是标准监督 ML 模型结构中无法获得的量。我们的方法将物理学原理与高精度 ML 相结合,可有效解决无法在实验中直接测量所需控制量的任何问题。这种方法可以自然地扩展到时间依赖性和开放式量子系统,并可应用于量子噪声光谱学和消除。
Experimental graybox quantum system identification and control
Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning (ML) methods, which provide efficient control solutions but no control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a ‘graybox’ approach to construct a physical model of a quantum system and use it to design optimal control. We report superior performance over model fitting, while generating unitaries and Hamiltonians, which are quantities not available from the structure of standard supervised ML models. Our approach combines physics principles with high-accuracy ML and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.
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.