  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. 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.
