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Special Session (Research Track): A Quantum Leap in Machine Learning: From Applications to Implementations
Event TypeSpecial Session (Research Track)
Virtual Programs
Hosted in Virtual Platform
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Design
Time
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DescriptionClassical machine learning techniques that have been extensively studied for discriminative and generative tasks are cumbersome and, in many applications, inefficient. They require millions of parameters and remain inadequate in modeling a target probability distribution. For example, computational approaches to accelerate drug discovery using machine learning face curse-of-dimensionality due to exploding number of constraints that need to be imposed using reinforcement learning. Quantum machine learning (QML) techniques, with strong expressive power, can learn richer representation of data with less number of parameters, training data and training time. However, the methodologies to design these QML workloads and their training is still emerging. Furthermore, usage model of the small and noisy quantum hardware in QML tasks to solve practically relevant problems is an active area of research. This special session will provide insights on building, training and exploiting scalable QML circuits to solve socially relevant combinatorial optimization applications including drug discovery.