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

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 quan-tum 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 neu-ronal sensing for near-term quantum processors and opens new avenues for exploring quantum many -body phenomena in larger-scale systems.(c) 2023 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.

Automated summary

This study proposes a new method called "quantum neuronal sensing" which utilizes a quantum processor to efficiently classify two different types of many-body phenomena: the ergodic and localized phases of matter. By measuring only one qubit, this method extracts the necessary information and offers better phase resolution than conventional methods. The research 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.