Characterizing and Benchmarking Dynamic Quantum Circuits

2026-04-03

AI Summary

This work addresses the limitations of existing quantum benchmarking tools, which primarily target unitary circuits and struggle to evaluate dynamic quantum circuits involving mid-circuit measurements and feedforward operations. To overcome this, the authors propose DynamARQ—a scalable, hardware-agnostic framework that introduces the first comprehensive benchmark suite for dynamic circuits, encompassing diverse representative applications. The framework defines structural features of dynamic circuits and integrates hardware execution data to devise application-specific fidelity scoring. Furthermore, it establishes a cross-platform, cross-calibration-period fidelity prediction model with parameter transferability. Experimental validation on IBM quantum processors and Quantinuum Helios-1E demonstrates the framework’s efficacy, achieving high-accuracy fidelity prediction and revealing strong correlations between circuit structural features and execution fidelity.

Abstract

Dynamic quantum circuits with mid-circuit measurements (MCMs) and feed-forward operations play a crucial role in various applications, such as quantum error correction and quantum algorithms. With advancements in quantum hardware enabling the implementation of MCM and feed-forward loops, the use of dynamic circuits has become increasingly prevalent. There is a significant need for a benchmarking framework specially designed for dynamic circuits to capture their unique properties, as current benchmarking tools are designed primarily for unitary circuits and cannot be trivially extended to dynamic circuits. We propose dynamarq, a scalable and hardware-agnostic benchmarking framework for dynamic circuits. We collect a set of dynamic circuit benchmarks spanning various applications and propose a broad set of circuit features to characterize the structure of these dynamic circuits. We run them on two IBM quantum processors and the Quantinuum Helios-1E emulator, and propose scalable, application-dependent fidelity scores for each benchmark based on hardware execution results. We perform statistical modeling to identify correlations between circuit features and fidelity scores, and demonstrate highly accurate fidelity prediction using our model. Our model parameters are also transferable across hardware backends and calibration cycles. Our framework facilitates the understanding of dynamic circuit structures and provides insights for designing and optimizing dynamic circuits to achieve high execution fidelity on quantum hardware.

Problem

• dynamic quantum circuits
• mid-circuit measurements
• feed-forward operations
• benchmarking framework
• quantum hardware

Innovation

• dynamic quantum circuits
• mid-circuit measurement
• benchmarking framework
• fidelity prediction
• hardware-agnostic