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arXiv 2206.02474quant-ph
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量子神经网络中的纠缠熵产生

Entanglement entropy production in Quantum Neural Networks

Marco Ballarin, Stefano Mangini, Simone Montangero, Chiara Macchiavello, Riccardo Mengoni

AI总结:

本文利用矩阵积态研究多达五十量子比特的随机参数 QNN,揭示其纠缠随深度趋向 Haar 随机态,并提出“纠缠速度”度量。

摘要:

量子神经网络(QNN)被认为是在含噪声中等规模量子计算机(NISQ)时代实现量子优势的候选方案。已有若干 QNN 架构被提出,并在机器学习的基准数据集上成功测试。然而,对 QNN 所产生纠缠的定量研究仅在至多少数几个量子比特上开展过。张量网络方法允许在多种场景下模拟具有大量量子比特的量子电路。在这里,我们采用矩阵积态来表征近期研究的、具有随机参数且规模多达五十个量子比特的 QNN 架构,表明随着 QNN 深度增加,其以量子比特间纠缠熵衡量的纠缠趋向于 Haar 分布随机态的纠缠。我们还通过测量电路的可表达性,以及利用随机矩阵理论的工具,认证了量子态的随机性。我们展示了任意给定 QNN 架构中纠缠产生速率的普适行为,并因此引入了一种表征 QNN 中纠缠产生的新度量:纠缠速度。我们的结果刻画了量子神经网络的纠缠性质,并为这些网络逼近随机酉算子的速率提供了新的证据。

英文摘要:

Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era. Several QNN architectures have been proposed and successfully tested on benchmark datasets for machine learning. However, quantitative studies of the QNN-generated entanglement have been investigated only for up to few qubits. Tensor network methods allow to emulate quantum circuits with a large number of qubits in a wide variety of scenarios. Here, we employ matrix product states to characterize recently studied QNN architectures with random parameters up to fifty qubits showing that their entanglement, measured in terms of entanglement entropy between qubits, tends to that of Haar distributed random states as the depth of the QNN is increased. We certify the randomness of the quantum states also by measuring the expressibility of the circuits, as well as using tools from random matrix theory. We show a universal behavior for the rate at which entanglement is created in any given QNN architecture, and consequently introduce a new measure to characterize the entanglement production in QNNs: the entangling speed. Our results characterise the entanglement properties of quantum neural networks, and provides new evidence of the rate at which these approximate random unitaries.

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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