Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying the 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 enormous numbers of interacting particles, classifying large-scale quantum states is often very challenging for classical approaches. Here, we propose a new approach using 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: the ergodic and localized phases of matter. The quantum neuronal process in the sensing allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum 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.