Bachelor Data Science and Artificial Intelligence Comparison of Classical, Hybrid Quantum-Classical, and Quantum Generative Models on the Low-Dimensional Bars and Stripes Dataset
Overall, the results show that quantum and hybrid quantum-classical generative models can learn non-trivial discrete probability distributions but that their effectiveness depends strongly on the selected quantum model, ansatz, and training objective.
Findings establish QCBM as a viable complementary tool for data augmentation, particularly for low-dimensional structured tabular data with class imbalance, particularly for low-dimensional structured tabular data with class imbalance.
Tanapol Nuatho, Narisorn Sangnakara, Prapong Prechaprapranwong et al.· 0 citations
Quantum machine learning (QML) is emerging as a key enabler of next-generation artificial intelligence (AI), offering more compact models and enhanced data processing capabilities. However, the integration of QML into AI-enabled network services can introduce new adversarial vulnerabilities, particularly the interface...
V. Nguyen, Yared Abera Ergu· IEEE Transactions on Network...· 0 citations
Simulated experiments show that this reformulate quantum image generation as coordinate-conditioned implicit function learning outperforms FRQI-based generation and PQWGAN in visual and quantitative quality while using fewer qubits, and also achieves better generation quality than the corresponding classical baseline.
Xue Yang, Rigui Zhou, Shi-Zheng Jia et al.· 0 citations
The development, deployment, and empirical analysis of a hybrid quantum-classical image classifier in the binary classification of handwritten digits and the results demonstrate that it is possible to manufacture successfully hybrid quantum-classical architectures with the help of existing open-source tools and simulat...
Haider Ali, Muhammad Azeem Akbar, A. Khan et al.· SIGSOFT FSE Companion· 0 citations
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through e...
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations
Noise-induced attacks that manipulate the Zero-Noise Extrapolation pipeline are the most damaging, followed by the QTrojan circuit-level backdoor, while the QDoor parameter-level backdoor is the least effective, yielding only marginal amplification.
Ahmed Azaz Humdoon, Cheng Chu, Lei Jiang et al.· arXiv.org· 0 citations
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