Quantum AI: Achievements and Challenges in the Interplay of Quantum Computing and Artificial Intelligence

Abstract

In recent times, Quantum Computing (QC) is receiving growing attention, thanks to the enormous advances in the construction of operational quantum computers, quantum materials and quantum cryptography. Given the advances in the physical construction and the scaling up of quantum computers, it is now necessary to foster the creation of quantum algorithms and methods that are adapted to such computers and that make the most of their intrinsic computational and communication capabilities. In the era of Big Data, some of the most computationally demanding tasks fall into the field of Artificial Intelligence (AI), including tasks that are currently computationally intractable due to physical limitations. The intrinsic parallelism, computational efficiency, and representational power offered by QC make for an excellent alternative to binary computers, holding the promise of enhanced AI models. This novel Quantum Artificial Intelligence (QAI) concept will result in the detection of patterns that classical AI algorithms are unable to identify, and in the time reduction of several orders of magnitude. In this review, we describe the scientific progresses in the confluence of AI and QC. We start by presenting both areas, basic concepts and the timeline of most significant advances in the history of AI and QC, to then focus on existing research made in the bidirectional approaches of QC benefiting from AI and AI benefiting from QC. Finally, we describe future avenues of research for the QAI incipient scientific area, and conclude.