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Proceedings of the AAAI Conference on Artificial Intelligence

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Adaptive Fidelity Estimation for Quantum Programs with Gr...
Tingting Li, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Tingting Li Shanghai Qi Zhi Institute Zhejiang University
  • Ziming Zhao Zhejiang University
  • Jianwei Yin Zhejiang University

DOI:

https://doi.org/10.1609/aaai.v40i1.37035

Abstract

Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.

How to Cite

Li, T., Zhao, Z., & Yin, J. (2026). Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 695–703. https://doi.org/10.1609/aaai.v40i1.37035

Issue

Section

AAAI Technical Track on Application Domains I

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