Quantum AI: Accomplishments and Obstacles in the Convergence of Quantum Computing and Artificial Intelligence

Abstract

Recently, Quantum Computing (QC) has garnered increasing attention due to significant advancements in the development of functional quantum computers, quantum materials, and quantum cryptography. In light of advancements in the physical construction and scaling of quantum computers, it is imperative to promote the development of quantum algorithms and methodologies tailored to these systems, maximising their inherent computational and communication capabilities. In the age of Big Data, several computationally intensive activities are within the domain of Artificial Intelligence (AI), encompassing those that are now computationally intractable owing to physical constraints. The inherent parallelism, computational efficiency, and representational capacity of quantum computing offers a superior alternative to binary computers, promising improved AI models. The Quantum Artificial Intelligence (QAI) idea will enable the identification of patterns that standard AI algorithms cannot detect, significantly reducing processing time by several orders of magnitude. This paper delineates the scientific advancements at the intersection of artificial intelligence and quality control. We commence by delineating both domains, fundamental concepts, and the chronology of pivotal advancements in the history of AI and QC, subsequently concentrating on the current study regarding the bidirectional methodologies wherein QC enhances AI and AI augments QC. Ultimately, we delineate prospective research directions for the nascent field of QAI and conclude.