Quantum Neuronal Sensing on a 61-Qubit NISQ Superconducting Processor

Noisy intermediate-scale quantum processors are considered an ideal platform to probe the complex physical nature of quantum many body systems. The growth of the quantum processor’s size however causes difficulties in extracting the relevant information, well beyond those associated with noise. It has long been known that the amount of information that one can extract from quantum states with a single measurement is limited.

We propose a new approach called “quantum neuronal sensing” that combines the computational power of QNNs with quantum sensing. Using our sixty-one qubit NISQ superconducting processor, we demonstrate that we can efficiently classify two different types of many-body phenomena (the localized and ergodic phases of matter). Our QNN learns the highly relevant features we require by directly processing that information without measurement while the sensing part allows us to extract that information by measuring only one qubit. The simplicity and efficiency of this approach demonstrates both the feasibility (and scalability) of quantum neuronal sensing on NISQ processors.