AI-Assisted Quantum Sensor for Noise Correlation

This paper presents a novel approach to characterize noise in quantum systems using a machine learning-assisted protocol. The use of two interacting qubits as a probe and the focus on classifying noise based on Markovianity and spatial correlations are significant contributions. The high accuracy achieved with minimal experimental overhead is also noteworthy, suggesting potential for practical applications in quantum computing and sensing.

• •Introduces a machine learning-assisted protocol for classifying noise correlations in quantum systems.
• •Utilizes two interacting qubits as a probe.
• •Discriminates noise based on Markovianity and spatial correlations.
• •Achieves high accuracy (around 90%) with minimal experimental overhead.