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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. 

Attackers are increasingly using model inversion attacks, in which the outputs of the model can be used to reconstruct confidential or private information to target machine learning models, especially those that handle sensitive financial data. We propose an attack model that exploits the output of classification models to infer details about the training data. We implement our experiments on the HPCC Systems platform. HPCC Systems is known for its robust data processing capabilities. Our approach systematically exploits the output of financial data-based classification models to reconstruct sensitive attributes, thereby demonstrating the potential risks and vulnerabilities resulting from an attack. In our research, we also have tested some defensive strategies to secure the model against inversion attack. 

Traditional Knowledge Graphs (KGs), such as Neo4j, face challenges in managing high-dimensional relationships and capturing semantic nuances due to their deterministic nature. Quantum Natural Language Processing (QNLP) introduces probabilistic reasoning into the KG context. This integration leverages quantum principles, such as superposition, which allows relationships to exist in multiple states simultaneously, and entanglement, where the state of one entity dynamically influences the state of another. This quantum-based probabilistic reasoning provides a richer, more flexible representation of connections, moving beyond binary relationships to model the nuances and variability of real-world interactions. Our research demonstrates that QNLP enhances Neo4j’s ability to analyze context-rich data, improving tasks like entity extraction nd knowledge inference. By modeling relationship states probabilistically, QNLP addresses limitations in traditional methods, providing nuanced insights and enabling more advanced, contextaware NLP applications. 

Machine learning has been successfully applied to big data analytics across various disciplines. However, as data is collected from diverse sectors, much of it is private and confidential. At the same time, one of the major challenges in machine learning is the slow training speed of large models, which often requires high-performance servers or cloud services. To protect data privacy while still allowing model training on such servers, privacy-preserving machine learning using Fully Homomorphic Encryption (FHE) has gained significant attention. However, its widespread adoption is hindered by performance degradation. This paper presents our experiments on training models over encrypted data using FHE. The results show that while FHE ensures privacy, it can significantly degrade performance, requiring complex tuning to optimize. 

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.