Efficient Fidelity Estimation with Few Local Pauli Measurements

• URL: http://arxiv.org/abs/2510.08155v1
• Date: Thu, 09 Oct 2025 12:35:58 GMT
• Title: Efficient Fidelity Estimation with Few Local Pauli Measurements
• Authors: Mingyu Sun, Gabriel Waite, Michael Bremner, Christopher Ferrie,
• Abstract summary: We develop a protocol for quantifying how close an experimental state aligns with a target.<n>We analyze the bias in this estimator, linking its performance to the mixing time $tau$ of a Markov chain induced by the target state.<n>This work enables scalable benchmarking, error characterization, and tomography assistance.
• Score: 3.428706362109922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As quantum devices scale, quantifying how close an experimental state aligns with a target becomes both vital and challenging. Fidelity is the standard metric, but existing estimators either require full tomography or apply only to restricted state/measurement families. Huang, Preskill, and Soleimanifar (Nature Physics, 2025) introduced an efficient certification protocol for Haar-random states using only a polynomial number of non-adaptive, single-copy, local Pauli measurements. Here, we adopt the same data collection routine but recast it as a fidelity estimation protocol with rigorous performance guarantees and broaden its applicability. We analyze the bias in this estimator, linking its performance to the mixing time $\tau$ of a Markov chain induced by the target state, and resolve the three open questions posed by Huang, Preskill, and Soleimanifar (Nature Physics, 2025). Our analysis extends beyond Haar-random states to state $t$-designs, states prepared by low-depth random circuits, physically relevant states and families of mixed states. We introduce a $k$-generalized local escape property that identifies when the fidelity estimation protocol is both efficient and accurate, and design a practical empirical test to verify its applicability for arbitrary states. This work enables scalable benchmarking, error characterization, and tomography assistance, supports adaptive quantum algorithms in high dimensions, and clarifies fundamental limits of learning from local measurements.

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