The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
It addresses foundational issues for researchers in quantum AI, but is incremental as it surveys and formalizes existing challenges without presenting new solutions.
The paper identifies key challenges in integrating quantum computing with artificial intelligence to accelerate machine learning processes, focusing on replacing iterative training, handling larger data, combining quantum-classical components, and verifying quantum benefits.
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.