在61比特可编程超导量子处理器上对量子多体态进行量子神经元感知

Quantum neuronal sensing of quantum many-body states on a 61-qubit programmable superconducting processor

对具有不同性质和物相的多体量子态进行分类是量子多体物理学中最基本的任务之一.然而,由于巨大数量的相互作用的粒子所产生的指数级的复杂性,大规模量子态的分类对于经典的方法来说极具挑战性.本文提出了一种新的方法,称为量子神经元感知.利用一个61比特的超导量子处理器作为演示,作者表明该方案可以有效地对两种不同类型的多体现象,即遍历相和局域相,进行分类.量子神经元感知过程使他们能够通过只测量一个量子比特来区分这些多体物相,并提供比传统方法(如测量不平衡度)更好的分辨率.本研究证明了量子神经元感知在近期量子处理器应用的可行性和扩展性,并为探索更大规模系统中的量子多体现象开辟了新的途径.

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 manybodyphenomena in larger-scale systems.