Characterizing and Benchmarking Dynamic Quantum Circuits

TL;DR

This paper introduces dynamarq, a scalable benchmarking framework for dynamic quantum circuits with mid-circuit measurements, enabling better understanding and optimization of their fidelity across hardware.

Contribution

It presents a novel, hardware-agnostic benchmarking framework for dynamic circuits, including features, fidelity scores, and predictive modeling transferable across hardware.

Findings

Dynamic circuits can be characterized by a broad set of features.

Fidelity scores correlate with circuit features and can be predicted accurately.

The framework's parameters are transferable across different hardware backends.

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.