Enhancing Quantum Software Development Process with Experiment Tracking

TL;DR

This paper discusses how implementing experiment tracking tools like MLflow can improve reproducibility, collaboration, and development practices in quantum computing research, especially as it becomes more experimental and hybrid.

Contribution

It introduces the application of MLflow for experiment tracking in quantum research, highlighting its benefits for reproducibility and collaboration in the evolving quantum landscape.

Findings

MLflow enhances experiment reproducibility in quantum research

Structured tracking workflows improve collaboration and decision making

Application of MLflow supports hybrid classical-quantum development environments

Abstract

As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment reproducibility, decision making, and cross-domain integration in an increasingly hybrid classical-quantum landscape.