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

Area of Science:

• Quantum Many-Body Physics
• Quantum Information Science
• Condensed Matter Physics

Background:

• Classifying quantum states and phases of matter is crucial but hindered by computational complexity.
• Classical methods struggle with the exponential scaling of interacting quantum systems.
• Distinguishing between different quantum phases, like ergodic and localized, is a fundamental challenge.

Purpose of the Study:

• To propose and demonstrate a novel quantum approach for classifying many-body quantum states.
• To overcome the limitations of classical computation in analyzing complex quantum systems.
• To efficiently differentiate between ergodic and localized phases of matter using quantum resources.

Main Methods:

• Development of a quantum neuronal sensing scheme.
• Implementation on a 61-qubit superconducting quantum processor.
• Analysis of eigenspectrum statistical characteristics for phase classification.

Main Results:

• Successful classification of ergodic and localized phases of matter.
• Demonstration of efficient information extraction by measuring only one qubit.
• Achieved superior phase resolution compared to conventional methods like measuring imbalance.

Conclusions:

• Quantum neuronal sensing is a feasible and scalable method for near-term quantum processors.
• The technique offers a powerful new tool for exploring quantum many-body phenomena.
• This approach opens avenues for studying larger and more complex quantum systems.