Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China, Hefei 230026, China; Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China; Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China.
Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China, Hefei 230026, China; Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China; Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China; Henan Key Laboratory of Quantum Information and Cryptography, Zhengzhou 450000, China.
Sci Bull (Beijing). 2023 May 15;68(9):906-912. doi: 10.1016/j.scib.2023.04.003. Epub 2023 Apr 7.
Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61-qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit and offers better phase resolution than conventional methods, such as measuring the imbalance. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.
对具有不同性质和物质相的多体量子态进行分类是量子多体物理中最基本的任务之一。然而，由于相互作用粒子数量的指数级增长，经典方法对大规模量子态的分类极具挑战性。在这里，我们提出了一种称为量子神经元感知的新方法。利用一个 61 量子比特超导量子处理器，我们表明我们的方案可以有效地对两种不同类型的多体现象进行分类：即物质的遍历相和局域相。我们的量子神经元感知过程允许我们通过仅测量一个量子比特来提取来自本征谱统计特征的必要信息，以区分这些物质相，并提供比传统方法（例如测量不平衡）更好的相位分辨率。我们的工作证明了量子神经元感知对于近期量子处理器的可行性和可扩展性，并为在更大规模系统中探索量子多体现象开辟了新途径。
