SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education

Quantum Artificial Intelligence for Natural Language Processing Applications

Natural Language Processing and Semantic Web include several NP complete/hard problems that are intractable for classical computing machines. Even though distributed computing has provided remarkable advances (more precisely in dealing with big data), non-decomposable NP problems are still intractable in many real-world applications. And, from a quantum computing perspective, solving complex problems with universal quantum gates requires the developing of quantum algorithms. Considering commercializing quantum annealing machines by D-Wave, achieving global optimum for discrete optimization problems has been realized. In this study, a novel approach has been introduced to convert symbolic AI problems into quadratic unconstrained binary optimization (QUBO) form. More narrowly, this method represents the classification of text documents (fragments) as optimizing a QUBO function. After embedding the training corpus into a QUBO function, D-Wave quantum annealer is used to classify new observations by finding the minimum energy level of the system.

natural language processing, quantum computing



Abstract only

UMBC ebiquity