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Quantum computing and artificial intelligence: status and perspectives

Giovanni Acampora, Andris Ambainis, Natalia Ares, Leonardo Banchi, Pallavi Bhardwaj, Daniele Binosi, G. Andrew D. Briggs, Tommaso Calarco, Vedran Dunjko, Jens Eisert, Olivier Ezratty, Paul Erker, Federico Fedele, Elies Gil-Fuster, Martin Gärttner, Mats Granath, Markus Heyl, Iordanis Kerenidis, Matthias Klusch, Anton Frisk Kockum, Richard Kueng, Mario Krenn, Jörg Lässig, Antonio Macaluso, Sabrina Maniscalco, Florian Marquardt, Kristel Michielsen, Gorka Muñoz-Gil, Daniel Müssig, Hendrik Poulsen Nautrup, Sophie A. Neubauer, Evert van Nieuwenburg, Roman Orus, Jörg Schmiedmayer, Markus Schmitt, Philipp Slusallek, Filippo Vicentini, Christof Weitenberg, Frank K. Wilhelm

TL;DR

The paper surveys the bidirectional interface between quantum computing and artificial intelligence, proposing a strategic EU research agenda to advance both foundational theory and practical applications. It outlines how quantum resources can accelerate AI and, conversely, how AI can accelerate quantum hardware, software, and data analysis, with emphasis on near-term hybrid approaches and long-term fully quantum visions. It details a broad research program across learning models, data processing, optimization, reasoning, and multi-agent systems, plus concrete use cases in healthcare, industry, and quantum physics. It concludes with actionable recommendations on theoretical work, hardware-roadmap alignment, resource estimation, software engineering, open science, education, and societal considerations to strengthen European competitiveness while addressing energy and ethical implications.

Abstract

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

Quantum computing and artificial intelligence: status and perspectives

TL;DR

The paper surveys the bidirectional interface between quantum computing and artificial intelligence, proposing a strategic EU research agenda to advance both foundational theory and practical applications. It outlines how quantum resources can accelerate AI and, conversely, how AI can accelerate quantum hardware, software, and data analysis, with emphasis on near-term hybrid approaches and long-term fully quantum visions. It details a broad research program across learning models, data processing, optimization, reasoning, and multi-agent systems, plus concrete use cases in healthcare, industry, and quantum physics. It concludes with actionable recommendations on theoretical work, hardware-roadmap alignment, resource estimation, software engineering, open science, education, and societal considerations to strengthen European competitiveness while addressing energy and ethical implications.

Abstract

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

Paper Structure

This paper contains 55 sections, 3 figures.

Table of Contents

  1. Executive summary
  2. Introduction
  3. Quantum for AI
  4. Quantum-assisted machine learning
  5. Supervised learning
  6. Reinforcement learning
  7. Unsupervised learning
  8. Research directions
  9. Learning models
  10. Quantum artificial intelligence for algorithmic discovery
  11. Quantum data pre-processing
  12. Quantum optimization
  13. Quantum reasoning
  14. Quantum algorithms for multi-agent systems
  15. Use cases and applications
  16. ...and 40 more sections

Figures (3)

  • Figure 1: An overview of the structure of this white paper.
  • Figure 2: Quantum AI (QAI) as the intersection of quantum computing and AI with subfields in relation to AI each covering both directions. Image adapted from Ref. klusch2024quantum.
  • Figure 3: Schematic of particular applications of AI for quantum computing, as presented in the different subsections of Section \ref{['sec:ai4q']}.