A Taxonomy of Relationships Between Information Processing, Machine Learning and Quantum Physics: Quantum-Inspired, Quantum-Assisted, Quantum-Targeted and Related Approaches

In this paper, we provide an overview of the connections that were developed, during the current second quantum revolution, between information processing (IP) and machine learning (ML) in the classical (i.e. non-quantum) framework on the one hand, and several aspects of quantum physics on the other hand, namely properties involved in quantum theory and practical implementations leading to quantum computers. Moreover, we propose a taxonomy of the new fields resulting from these connections, both at the general level of IP methods and at the more specific level of data-driven, i.e. ML, methods. We thus consider quantum-inspired (or quantum-like), quantum-assisted (or quantum-enhanced), quantum-executed and quantum-targeted IP and ML methods.