Multi-Layer Cycle Benchmarking for high-accuracy error characterization

TL;DR

Multi-Layer Cycle Benchmarking (MLCB) enhances quantum noise characterization by jointly analyzing multiple gate layers, significantly improving learnability and accuracy of Pauli noise models, thereby advancing error mitigation in quantum computing.

Contribution

MLCB introduces a scalable protocol that jointly analyzes multiple Clifford gate layers to improve learnability of Pauli noise models in quantum systems.

Findings

MLCB reduces unlearnable noise degrees of freedom by up to 75%.

MLCB improves the accuracy of sparse Pauli-Lindblad noise models.

MLCB enhances the performance of error mitigation techniques like probabilistic error cancellation.

Abstract

Accurate noise characterization is essential for reliable quantum computation. Effective Pauli noise models have emerged as powerful tools, offering detailed description of the error processes with a manageable number of parameters, which guarantees the scalability of the characterization procedure. However, a fundamental limitation in the learnability of Pauli fidelities impedes full high-accuracy characterization of both general and effective Pauli noise, thereby restricting e.g., the performance of noise-aware error mitigation techniques. We introduce Multi-Layer Cycle Benchmarking (MLCB), an enhanced characterization protocol that improves the learnability associated with effective Pauli noise models by jointly analyzing multiple layers of Clifford gates. We show a simple experimental implementation and demonstrate that, in realistic scenarios, MLCB can reduce unlearnable noise degrees of freedom by up to $75\%$, improving the accuracy of sparse Pauli-Lindblad noise models and boosting the performance of error mitigation techniques like probabilistic error cancellation. Our results highlight MLCB as a scalable, practical tool for precise noise characterization and improved quantum computation.