| G. Drakopoulos, Ph. Mylonas |
| Graph Quantum Machine Learning: A Survey |
| International Conference on Human-AI Collaboration & Augmented Intelligence (HAICAI 2026), 23-24 Arpil 2026, Athens, Greece |
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ABSTRACT
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| Quantum computing has already given some early major results being a disruptive computing paradigm. In the latter massive parallelism is feasible through superposition and entanglement, properties holding true irrespective of the particular implementation technology. Both properties make quantum computing appropriate for intractable combinatorial problems. Among the latter, graph problems constitute a prominent class not only because of the the versatility of graphs and the numerous applications, but also because many NP complete problems are expressed as graph problems, such as graph coloring or vertex cover, or can be reduced to one in polynomial number of steps, like the ubiquitous SAT. In this context, graph machine learning is a promising path to obtaining new insight into notorious computational problems as well as solving them efficiently of course. The various mainstays of quantum computing and graph quantum machine learning are described, early results are presented, and possible future directions are summarized.
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| 23 April , 2026 |
| G. Drakopoulos, Ph. Mylonas, "Graph Quantum Machine Learning: A Survey", International Conference on Human-AI Collaboration & Augmented Intelligence (HAICAI 2026), 23-24 Arpil 2026, Athens, Greece |
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