From classical to quantum machine learning through tensor networks

Machine learning is a data-driven field, which needs massive computing resources. Quantum computation, on the other hand, can provide exponential speedups for some classical algorithms. Therefore, it is natural to combine the strengths of both fields to solve outstanding problems in industry and research. The project has three goals. The first goal is to use machine learning methods for the description of many-body quantum systems. In this part of the project, we will tackle some of the notable problems of many-body quantum mechanics with new tools that are emerging by adopting neural networks to quantum mechanical problems. The second goal is to use methods from many-body quantum mechanics to describe machine learning problems. We will address the problems of adversarial examples, uncertainty, and generalization from a new perspective, which is motivated by the success of tensor networks for a description of many-body quantum systems. The third and most ambitious goal is to combine the knowledge from quantum mechanics and machine learning to find novel applications of noisy intermediate-scale quantum devices with significant speedups for known classical algorithms. We will apply a combination of successful quantum-mechanical tools and advanced machine learning tools to find useful quantum algorithms that could demonstrate applied quantum advantage.