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Creators/Authors contains: "Polisetty, Andrew"

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  1. ; ; ; ; ; (, ACM)
    Quantum-based Machine Learning (QML) combines quantum computing (QC) with machine learning (ML), which can be applied in various sectors, and there is a high demand for QML professionals. However, QML is not yet in many schools’ curricula. We design labware for the basic concepts of QC, ML, and QML and their applications in science and engineering fields in Google Colab, applying a three-stage learning strategy for efficient and effective student learning. 
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    Free, publicly-accessible full text available February 18, 2026
  2. ; ; ; ; (, IEEE)
    In today’s rapidly evolving technology era, cybersecurity threats have become sophisticated, challenging conventional detection and defense. Classical machine learning aids early threat detection but lacks real-time data processing and adaptive threat detection due to the reliance on large, clean datasets. New attack techniques emerge daily, and data scale and complexity limit classical computing. Quantum-based machine learning (QML) using quantum computing (QC) offers solutions. QML combines QC and machine learning to analyze big data effectively. This paper investigates multiple QML algorithms and compares their performance with their classical counterparts. 
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    Free, publicly-accessible full text available December 10, 2025