Quantum Artificial Intelligence opportunities, challenges, threats, use case and future trends
TL;DR
QAI research focuses on using quantum computing to solve computationally challenging AI problems and, conversely, using AI to address the difficulties associated with developing and running quantum computing systems.
Abstract
The field of quantum computing is presently undergoing a significant transition. Even if quantum computers are now so large and capable that they can only be used under certain limitations, be conventionally emulated, it is still unknown which applications can benefit from a quantum advantage. With an emphasis on artificial intelligence (AI) and its applications as some of the most significant key technologies of our time, this provided an excellent starting point for looking beyond the already demonstrated acceleration of a few specific classical computing tasks by quantum computers, such as unstructured search and prime factorization. Actually, the goal of Quantum Artificial Intelligence (QAI) is to merge the two worlds for each’s benefit. Stated differently, QAI research focuses on using quantum computing to solve computationally challenging AI problems and, conversely, using AI to address the difficulties associated with developing and running quantum computing systems. The creation of direct quantum or hybrid quantum-classical algorithms for problem solving and the examination of their possible benefits over the best classical alternatives are of particular interest.