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industrial problems can benefit from novel computational paradigms, including quantum annealing, gate-based quantum optimization, quantum-inspired methods, and hybrid quantum-classical algorithms
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available in the Computer Science department at Purdue University. The research focuses on developing algorithms for the analysis of time series data using quantum machine learning models, and
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contributes to applied nuclear and particle physics research leveraging machine learning and AI for data analysis and detector development, as well as exploratory work in quantum algorithms, depending on
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