Understanding quantum advantage in machine learning

Understanding quantum advantage in machine learning

In this talk, Prof. Apoorva Patel will be talking about how machine learning models enable pattern recognition and big data analysis without direct human intervention. Supervised learning classifies new data based on learned patterns, while unsupervised learning identifies probability distributions to make predictions. The former uses feature maps, while the latter uses Boltzmann distributions, both with a large number of tunable parameters. Quantum extensions of these models replace classical probability distributions with the quantum density matrix. An advantage can be obtained only when features of quantum density matrices that are missing in classical probability distributions are exploited. Such situations depend on the input data as well as the targeted observables. Illustrative examples bring out the constraints limiting possible quantum advantage. The problem-dependent extent of quantum advantage has implications for both data analysis and sensing applications.