Enhancing Quantum Software Development Process with Experiment Tracking

2025-07-09

AI Summary

Reproducibility, scalability, and cross-team collaboration in quantum computing experiments remain significant challenges. To address these, this work introduces a structured experimental tracking workflow—marking the first systematic adoption of the MLflow framework in quantum research. It enables unified, standardized logging of parameters, metrics, source code, and quantum circuits within hybrid classical-quantum experiments. The approach seamlessly integrates mainstream quantum programming frameworks—including Qiskit and PennyLane—and supports versioned experiment records and exact result reproducibility. Empirical evaluation demonstrates that the proposed methodology substantially improves reproducibility and collaborative efficiency in quantum software development, reduces knowledge retention overhead, and establishes a scalable engineering infrastructure for large-scale quantum algorithm development and interdisciplinary collaboration.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

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.

Problem

• Enhancing reproducibility in quantum software development
• Improving scalability of quantum research workflows
• Facilitating collaboration in hybrid classical-quantum systems

Innovation

• Applying MLflow to quantum research
• Enhancing reproducibility with structured workflows
• Integrating classical-quantum development practices