Multi-Layer Cycle Benchmarking for high-accuracy error characterization

• URL: http://arxiv.org/abs/2412.09332v1
• Date: Thu, 12 Dec 2024 15:00:31 GMT
• Title: Multi-Layer Cycle Benchmarking for high-accuracy error characterization
• Authors: Alessio Calzona, Miha Papič, Pedro Figueroa-Romero, Adrian Auer,
• Abstract summary: We introduce Multi-Layer Cycle Benchmarking (MLCB) to improve the learnability associated with effective Pauli noise models. In realistic scenarios, MLCB can reduce unlearnable noise degrees of freedom by up to $75%$, improving the accuracy of sparse Pauli-Lindblad noise models. Our results highlight MLCB as a scalable, practical tool for precise noise characterization and improved quantum computation.
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• 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.

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