Direct entanglement detection of quantum systems using machine learning

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Yulei Huang, Liangyu Che, Chao Wei, Feng Xu, Xinfang Nie, Jun Li, Dawei Lu, Tao Xin
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Abstract

Entanglement plays a crucial role in advancing quantum technologies and exploring quantum many-body simulations. Here, we introduce a protocol aided by neural networks for measuring entanglement in both equilibrium and non-equilibrium states of local Hamiltonians, with a favorable amount of training data. Our numerical simulations across various Hamiltonian models and qubit configurations reveal that this approach can predict comprehensive entanglement metrics, such as Rényi entropy, for up to 100 qubits using only single-qubit and two-qubit Pauli measurements. Excitingly, future entanglement dynamics beyond the measurement window can be predicted based solely on previous single-qubit traces. Experimentally, we utilize a nuclear spin quantum processor and a neural network to measure entanglement in the ground and dynamical states of a one-dimensional spin chain. The results demonstrate the feasibility of our method in practical experiments. Therefore, our approach offers a promising method for experimentally measuring entanglement in systems with dozens to hundreds of qubits.

Abstract Image

使用机器学习的量子系统直接纠缠检测
纠缠在推进量子技术和探索量子多体模拟中起着至关重要的作用。在这里,我们引入了一个由神经网络辅助的协议,用于测量局部哈密顿量在平衡和非平衡状态下的纠缠,并提供了大量的训练数据。我们对各种哈密顿模型和量子位配置的数值模拟表明,这种方法可以仅使用单量子位和双量子位泡利测量来预测多达100个量子位的综合纠缠度量,例如r熵。令人兴奋的是,超越测量窗口的未来纠缠动力学可以仅基于先前的单量子比特轨迹来预测。实验上,我们利用核自旋量子处理器和神经网络来测量一维自旋链的基态和动态纠缠态。实验结果表明了该方法的可行性。因此,我们的方法为实验测量具有数十到数百个量子比特的系统中的纠缠提供了一种很有前途的方法。
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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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