What is it about?
Artificial intelligence, and machine learning in particular, is used to solve many kinds of problems. Quantum machine learning promises to change how programs compute and to make software better at solving problems. However, using quantum computing is not easy and needs skills from several fields. In this article, we present QuantuMoonLight, a community-based low-code platform. It lets researchers and practitioners set up and run experiments with quantum machine learning pipelines, compare them with classical machine learning algorithms, and share lessons learned and experience reports.
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Why is it important?
QuantuMoonLight lowers the barrier to experimenting with quantum machine learning, because users do not need to write much code. We describe its architecture and main features, and discuss the impact we expect it to have.
Perspectives
Quantum machine learning is promising, but it is hard to try without specific skills. QuantuMoonLight is our attempt to lower this barrier: a low-code platform where researchers and practitioners can set up quantum machine learning pipelines, compare them with classical algorithms, and share what they learned. It was an early step in my work on quantum software engineering.
Prof. Stefano Lambiase
Aalborg Universitet
Read the Original
This page is a summary of: QuantuMoonLight: A low-code platform to experiment with quantum machine learning, SoftwareX, May 2023, Elsevier,
DOI: 10.1016/j.softx.2023.101399.
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