QUANTUMOONLIGHT: A low-code platform to experiment with quantum machine learning

Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms. Recent advances revolve around the use of quantum machine learning, which promises to revolutionize program computation and boost software systems' problem-solving capabilities. However, using quantum computing technologies is not trivial and requires interdisciplinary skills and expertise. For such a reason, we propose QUANTUMOONLIGHT, a community-based low-code platform that allows researchers and practitioners to configure and experiment with quantum machine learning pipelines, compare them with classic machine learning algorithms, and share lessons learned and experience reports. We showcase the architecture and main features of QUANTUMOONLIGHT, other than discussing its envisioned impact on research and practice.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Machine learning is widely used by software engineering researchers to solve problems in various research fields. Quantum machine learning has recently gained attention for its potential to revolutionize program computation and enhance problem-solving capabilities. To facilitate the use of quantum computing technologies, we propose a community-based low-code platform called QUANTUMOONLIGHT, which enables researchers and practitioners to configure and experiment with quantum machine learning pipelines, compare them with traditional algorithms, and share their experiences. This article introduces the architecture and key features of QUANTUMOONLIGHT and discusses its expected impact on research and practice.