  Quantum learning paradigms address the question of how best to harness
conceptual elements of quantum mechanics and information processing to improve
operability and functionality of a computing system for specific tasks through
experience. It is one of the fastest evolving framework, which lies at the
intersection of physics, statistics and information processing, and is the next
frontier for data sciences, machine learning and artificial intelligence.
Progress in quantum learning paradigms is driven by multiple factors: need for
more efficient data storage and computational speed, development of novel
algorithms as well as structural resonances between specific physical systems
and learning architectures. Given the demand for better computation methods for
data-intensive processes in areas such as advanced scientific analysis and
commerce as well as for facilitating more data-driven decision-making in
education, energy, marketing, pharmaceuticals and health-care, finance and
industry.
