Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness

2026-01-21

AI Summary

This work addresses the challenge of unpredictable measurement overhead in fidelity estimation on noisy intermediate-scale quantum (NISQ) devices, where hardware noise, device heterogeneity, and compiler-induced circuit transformations complicate a priori resource allocation. To this end, we propose QuFid, a novel framework that, for the first time, integrates circuit structure, compilation-induced distortions, and noise propagation into a unified model. QuFid represents quantum programs as directed acyclic graphs (DAGs) and employs control-flow-aware random walks combined with runtime statistical feedback to enable noise-aware adaptive measurement allocation. Leveraging spectral analysis to quantify circuit complexity, QuFid delivers a lightweight yet theoretically grounded sampling strategy. Empirical evaluation across 18 benchmark circuits demonstrates that QuFid substantially reduces measurement costs compared to fixed-sampling and learning-based baselines while maintaining acceptable fidelity estimation bias.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Fidelity estimation
• NISQ devices
• measurement cost
• hardware noise
• circuit transpilation

Innovation

• adaptive fidelity estimation
• noise-aware quantum computing
• graph-guided random walk
• transpilation-induced deformation
• spectral complexity quantification