Can artificial intelligence benefit from quantum computing?
In this article, we will try to present from an academic point of view some of the relevant characteristics of both artificial intelligence (AI) and quantum computing (QC) in order to explore the possibility of a meaningful cooperation between both areas of computer science (CS). The quantum part of this paper is based on The Quantum Circuit Model which, in the opinion of the author, could be easier understood by computer scientists and/or artificial intelligence researchers, rather than other approaches such as, for example, Adiabatic Quantum Computation. Many fundamental questions will arise along this paper for which we will need to give an answer in order to analyze the basic principles that allow researchers and engineers to put AI and QC working together. With this in mind, we will briefly describe the behavior of the biological brain focusing on the identification of the singularities that allow them to perform in such an efficient manner. Energy consumption and parallelism are in the core of the above-mentioned efficiency. Then we will present some of the well-established artificial intelligence approaches that are potentially related to the operation of biological brains. After identifying common characteristics we will introduce basic concepts and issues related to the so-called reversible computing and QC that may eventually help to increase the efficiency of our current intelligent systems. In this respect, we will pay special attention to speed and energy consumption. Some examples, as well as an outline of algorithms from both quantum computing and artificial intelligence, will be used to illustrate the ideas presented in the paper. We conclude with a discussion about what can we expect from the cooperation between both, apparently unconnected fields of computer science, artificial intelligence and quantum computing.
artificial intelligence quantum computing reversible computing