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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.16915 |
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| _version_ | 1866911462103973888 |
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| author | Palacio, Maria Jose Lozano Nayfeh, Hasan Ware, Matthew McKay, David C. |
| author_facet | Palacio, Maria Jose Lozano Nayfeh, Hasan Ware, Matthew McKay, David C. |
| contents | With the continued scaling of quantum processors, holistic benchmarks are essential for extensively evaluating device performance. Layer fidelity is a benchmark well-suited to assessing processor performance at scale. Key advantages of this benchmark include its natural alignment with randomized benchmarking (RB) procedures, crosstalk awareness, fast measurements over large numbers of qubits, high signal-to-noise ratio, and fine-grained information. In this work, we extend the analysis of the original layer fidelity manuscript to optimize parameters of the benchmark and extract deeper insights of its application. We present a robust protocol for identifying optimal qubit chains of arbitrary length N, demonstrating that our method yields error per layered gate (EPLG) values 40% - 70% lower than randomly selected chains for N=100 qubits. We further establish layer fidelity as an effective performance monitoring tool, capturing both edge-localized and device-wide degradation by tracking optimal chains of length 50 and 100, and fixed chains of length 100. Additionally, we refine error analysis by proposing parameter bounds on the number of randomizations and Clifford lengths used in direct RB fits, minimizing fit uncertainties. Finally, we use layer fidelity to analyze the impact of varying gate durations on layered two-qubit (2Q) errors, showing that prolonged gate times leading to idling times significantly increase these quantities. These findings extend the applicability of the layer fidelity benchmark and provide practical guidelines for optimizing quantum processor evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter Analysis and Optimization of Layer Fidelity for Quantum Processor Benchmarking at Scale Palacio, Maria Jose Lozano Nayfeh, Hasan Ware, Matthew McKay, David C. Quantum Physics With the continued scaling of quantum processors, holistic benchmarks are essential for extensively evaluating device performance. Layer fidelity is a benchmark well-suited to assessing processor performance at scale. Key advantages of this benchmark include its natural alignment with randomized benchmarking (RB) procedures, crosstalk awareness, fast measurements over large numbers of qubits, high signal-to-noise ratio, and fine-grained information. In this work, we extend the analysis of the original layer fidelity manuscript to optimize parameters of the benchmark and extract deeper insights of its application. We present a robust protocol for identifying optimal qubit chains of arbitrary length N, demonstrating that our method yields error per layered gate (EPLG) values 40% - 70% lower than randomly selected chains for N=100 qubits. We further establish layer fidelity as an effective performance monitoring tool, capturing both edge-localized and device-wide degradation by tracking optimal chains of length 50 and 100, and fixed chains of length 100. Additionally, we refine error analysis by proposing parameter bounds on the number of randomizations and Clifford lengths used in direct RB fits, minimizing fit uncertainties. Finally, we use layer fidelity to analyze the impact of varying gate durations on layered two-qubit (2Q) errors, showing that prolonged gate times leading to idling times significantly increase these quantities. These findings extend the applicability of the layer fidelity benchmark and provide practical guidelines for optimizing quantum processor evaluations. |
| title | Parameter Analysis and Optimization of Layer Fidelity for Quantum Processor Benchmarking at Scale |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2510.16915 |