Enhancing Quantum Software Development Process with Experiment Tracking

• URL: http://arxiv.org/abs/2507.06990v1
• Date: Wed, 09 Jul 2025 16:14:18 GMT
• Title: Enhancing Quantum Software Development Process with Experiment Tracking
• Authors: Mahee Gamage, Otso Kinanen, Jake Muff, Vlad Stirbu,
• Abstract summary: Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that tracking, scalability, and collaboration in quantum research can benefit significantly from structured tracking.<n>This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment, decision making, and cross-domain integration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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