Speaker
Description
At the intersection of two rapidly growing fields, generative quantum machine learning research is attracting significant interest. Being in the early days of the field, the proposed algorithms mostly rely on generic quantum models, that while being very powerful, face several challenges. This motivates the design of problem-informed models, making assumptions about the data that can be encoded into the quantum circuit. Probabilistic graphical models provide a general mathematical framework to represent structure in learning problems involving random variables. In this work, we introduce a problem-informed quantum model that leverages the Markov network structure of the underlying problem. We further demonstrate the applicability of the Markov network framework in the construction of generative learning benchmarks and compare the performance of our model to previous designs. Finally, we make a distinction between quantum advantage in learning and sampling tasks, and discuss the potential of our model to demonstrate improvement over classical methods in sampling efficiency.
