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arXiv 2506.22016quant-ph
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基于电路量子电动力学系统的实验量子共振计算

Experimental quantum reservoir computing with a circuit quantum electrodynamics system

Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay(阿尔伯特·费尔特实验室,法国国家科学研究中心,泰雷兹集团,巴黎萨克雷大学);SPEC, CEA(特殊环境与条件物理研究部,法国原子能委员会)

Baptiste Carles, Julien Dudas, Léo Balembois, Julie Grollier, Danijela Marković

AI总结:

本研究利用电路量子电动力学系统实现了量子共振计算的实验验证,通过非线性特征提取实现了高效分类,展示了硬件高效的量子神经网络方法。

摘要:

量子共振计算是一种机器学习框架,相较于其他量子神经网络,其训练更容易,因为它不依赖于基于梯度的优化。学习是在单步中通过对量子系统的输出特征进行测量来完成的。已经探讨了各种量子共振计算的模拟实现,使用了不同的测量特征。尽管模拟显示量子共振器在性能上优于经典共振器,但实验实现仍然很少。这主要是由于获得大量非线性变换输入数据的输出特征的挑战。在本工作中,我们证明即使使用一个简单的电路量子电动力学系统,如一个耦合到读出谐振器的单个超导谐振子,也可以实现量子共振计算的原理验证。我们通过将输入数据编码在相干驱动的幅度中,并在福克基底中测量谐振腔状态,从单个物理系统中获得大量非线性特征。我们展示了对两个经典任务的分类,其硬件资源和测量特征比经典神经网络要小得多。我们的实验结果得到了数值模拟的支持,显示额外的克尔非线性对共振器性能是有益的。我们的工作展示了一种硬件高效的量子神经网络实现,可以进一步扩展并推广到其他量子机器学习模型。

英文摘要:

Quantum reservoir computing is a machine learning framework that offers ease of training compared to other quantum neural networks, as it does not rely on gradient-based optimization. Learning is performed in a single step on the output features measured from the quantum system. Various implementations of quantum reservoir computing have been explored in simulations, with different measured features. Although simulations have shown that quantum reservoirs present advantages in performance compared to classical reservoirs, experimental implementations have remained scarce. This is due to the challenge of obtaining a large number of output features that are nonlinear transformations of the input data. In this work, we show that even with a circuit quantum electrodynamics system as simple as a single transmon coupled to a readout resonator, we can implement a proof-of-concept realization of quantum reservoir computing. We obtain a large number of nonlinear features from a single physical system by encoding the input data in the amplitude of a coherent drive and measuring the cavity state in the Fock basis. We demonstrate classification of two classical tasks with significantly smaller hardware resources and fewer measured features compared to classical neural networks. Our experimental results are supported by numerical simulations that show additional Kerr nonlinearity is beneficial to reservoir performance. Our work demonstrates a hardware-efficient quantum neural network implementation that can be further scaled up and generalized to other quantum machine learning models.

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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