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

TL;DR

QuFid is an adaptive, noise-aware framework that efficiently estimates quantum program fidelity by modeling noise propagation through circuit structure, reducing measurement costs on real quantum hardware.

Contribution

It introduces QuFid, a novel graph-guided, adaptive fidelity estimation method that accounts for hardware noise and circuit structure in real-time measurement planning.

Findings

QuFid reduces measurement costs significantly compared to baselines.

It maintains fidelity accuracy while adapting to noise during execution.

Experimental results on IBM Quantum devices validate its effectiveness.

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.