Pauli Noise Learning for Mid-Circuit Measurements.

Area of Science:

• Quantum Information Science
• Quantum Computing Hardware

Background:

• Current benchmarks for midcircuit measurements (MCMs) lack scalability and comprehensive error quantification capabilities.
• There is a need for advanced techniques to accurately assess MCM performance in quantum systems.

Purpose of the Study:

• Introduce a novel theory for learning stochastic Pauli noise in MCMs.
• Develop a scalable method, MCM cycle benchmarking, for evaluating MCM performance.

Main Methods:

• Developed MCM cycle benchmarking, a scalable technique for MCM performance evaluation.
• Applied a theory for learning stochastic Pauli noise within MCMs.
• Extracted error rates from randomly compiled MCM and Clifford gate layers.

Main Results:

• Demonstrated the quantification of correlated errors during MCMs on current quantum hardware.
• Showcased the scalability of MCM cycle benchmarking for detailed error analysis.
• Validated the method's ability to extract error rates in complex quantum circuits.

Conclusions:

• MCM cycle benchmarking provides a scalable approach to characterize MCM errors.
• The method can be integrated with existing techniques for comprehensive quantum error analysis.
• This work advances the ability to quantify and mitigate errors in quantum hardware utilizing MCMs.