Machine Learning with Near-Term Quantum Computers

Recent studies on quantum computing simulators and hardware indicate that parametrized quantum circuits used for learning on classical data can achieve results similar to that of classical machine learning models while using significantly fewer parameters. We will present results from these studies in a variety of application areas ranging from image recognition to modeling financial data on up to 20 qubits. We will then show that the origin of this quantum advantage can be related to the hardness of modeling correlations present on small scales or in the tails of multivariate data distributions. In particular, we show that quantum computers can model arbitrary multivariate distributions with a number of parameters that scale linearly in the number of variables (features), whereas models used in classical machine learning will typically use an exponential number of parameters. We show that these arguments holds for both discriminative and generative learning, and leads to quantum models that are suitable for near-term quantum computers. We will also propose a metric that can a priori identify suitability of a particular dataset for either classical or quantum machine learning models.