Promoting Quantum-based Machine Learning through Multifaceted Activities

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Authors: Yong Shi, Dan Chia-Tien Lo, Valentina Nino, Hongmei Chi, Kun Suo, Tu Nguyen
Cited by: 0
References: 1

Abstract

Quantum-based machine learning (QML) is known to integrate quantum computing (QC) and machine learning (ML) to boost performance in data analysis and decision-making. The demand for professionals in QML has increased; however, QML is not part of the curricula for most colleges and universities. In this project, we create labware in Google Colab that encompasses knowledge of QC, ML, and QML, and demonstrates QML applications for analyzing scientific and engineering data. We integrate our learning modules into courses in Computer Science, Industrial Engineering, Physics, and other disciplines, and host a faculty workshop, a student camp, and a conference tutorial session to promote the use of our learning modules. The feedback from participating faculty and students is overwhelmingly positive.

Fields of Study & Subjects

Quantum Computing Algorithms and Architecture Teaching and Learning Programming Genetics Bioinformatics and Biomedical Research

Publication details

Journal / source
ACM Technical Symposium on Computer Science Education (SIGCSE)
Pages
1519–1520
Publication date
13 February 2026
Language
EN