Deep Quantum Learning

Abstract

It is human nature to stretch the limits of our knowledge through learning. Cultivating and developing the art of optimizing each little corner of our lives through continuous learning has been the backbone of human progress through the history of civilization. We benefit from efforts to advance various parts of our daily lives. Chapter 7 dealt with quantum annealing, that while optimization theories originate from mathematical concepts, they can also be transformed and built into physics. For example, physics has the principle of least action, the principle of minimum entropy generation, and the variational principle. Physics offers physical annealing, which preceded computationally simulated annealing. Physics also offers the adiabatic principle, which, in its quantum form, is quantum annealing. Hence, physical machines can solve the mathematical problem of optimization, including constraints—a property that can be extended to using quantum systems to treat classical and quantum data.