Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/76E7D78C-197D-43CC-8314-AAADC0DA86A8 | ||
| Year | 2025 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Athanasios Karakos, "Hybrid quantum classical algorithms for machine learning and optimization and applications in transport and scheduling problems", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2025 https://doi.org/10.26233/heallink.tuc.104757 | ||
| Appears in Collections |
This thesis investigates hybrid quantum-classical algorithms with a focus on Quadratic Unconstrained Binary Optimization (QUBO) formulations and their application to transport and scheduling problems. It begins with an overview of fundamental concepts in quantum mechanics, including qubits, quantum gates, and entanglement, to provide the necessary background. Building on this foundation, we analyze QUBO formulations and hybrid algorithms such as QAOA, VQA, ADAPT-QAOA, and qubit-efficient encoding schemes. We further explore the role of hybrid algorithms in generative AI, where transformer-based architectures are employed to generate parameterized quantum circuits. A substantial part of the work is devoted to a multimodal transport scheduling problem, modeled using integrated QUBO formulations. Simulations were carried out on both classical and quantum backends. The results show that quantum algorithms can deliver competitive solutions and highlight their strong potential for scalability as quantum hardware advances.
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