Rethinking Reproducibility in the Classical (HPC)-Quantum Era: Toward Workflow-Centered Science

TL;DR

This paper explores the challenges of reproducibility in classical and quantum computing, emphasizing the need for workflow-centered practices to enhance scientific rigor amid growing computational complexity.

Contribution

It introduces a workflow-centered approach with meta-workflows to improve reproducibility in classical-quantum scientific processes, addressing current methodological limitations.

Findings

Classical HPC faces reproducibility issues from hardware and documentation.

Quantum computing's probabilistic nature complicates reproducibility.

Workflow meta-approach offers a path to more robust scientific validation.

Abstract

Scientific knowledge increasingly depends on complex computational processes where both hardware and software layers can influence research outcomes. As computational complexity grows, classical-quantum integration provides a lens for examining how the scientific method adapts, particularly regarding a foundational principle of scientific validation - reproducibility. Building upon previous warnings of an ongoing reproducibility crisis in the computational context, this paper examines challenges across classical (HPC) and quantum computing. Despite its deterministic nature, HPC faces reproducibility threats from hardware dependencies, documentation inadequacies, disincentivizing research culture and infrastructure variation. Quantum computing, at low technological maturity, amplifies some challenges, while creating new ones through probabilistic outputs, hardware-specific noise, and tight software-hardware coupling. Classical-quantum integration reveals a telling pattern, where current reproducibility frameworks prove inadequate, as infrastructure blends with the results. Quantum integration serves as a catalyst exposing methodological limitations across the computational domain. We propose a workflow-centered path forward, pointing to the value of gradual cultural shift toward workflow-centered scientific practice. By developing meta-workflows that document both process abstractions and implementation contexts, we create a more robust foundation for scientific knowledge that acknowledges complexity without sacrificing rigor. The path forward involves embracing this evolution in understanding scientific knowledge rather than resisting it