The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline

TL;DR

This paper explores the integration of quantum computing with artificial intelligence, identifying four key challenges to accelerate machine learning processes and enhance quantum AI development.

Contribution

It provides a formal model for quantum AI and outlines four major challenges to advance the field beyond current approaches.

Findings

Identified four major challenges for quantum AI development.

Surveyed current approaches and related them to a formal model.

Highlighted the need for tools to verify quantum advantages.

Abstract

We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial intelligence: (i) Replace iterative training with faster quantum algorithms, (ii) distill the experience of larger amounts of data into the training process, (iii) allow quantum and classical components to be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm.