Quantum computing and artificial intelligence: status and perspectives

2025-05-29

AI Summary

Quantum computing and artificial intelligence (AI) face mutual bottlenecks—quantum hardware development is hampered by classical computational limits, while AI models confront fundamental scalability and energy-efficiency constraints. Method: This study establishes a novel “theory–hardware–engineering–society” quadruple-coordinated, full-stack research framework. It integrates quantum algorithm design, multi-paradigm machine learning, quantum compilation optimization, and techno-policy assessment to systematically investigate bidirectional synergies: (i) quantum-enhanced AI capabilities and (ii) classical AI–accelerated quantum hardware development (e.g., quantum sensing, compiler optimization). Contribution/Results: The work introduces a quantum AI software engineering paradigm and a cross-modal resource estimation methodology—including energy consumption. It delivers a forward-looking quantum AI roadmap for 2030+, articulating seven interdisciplinary implementation recommendations and a prioritized critical challenges inventory. These outputs directly support the European Quantum Flagship and the EU’s Sustainable AI Strategy.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Exploring intersections between quantum computing and artificial intelligence
• Developing innovative AI solutions using quantum computing
• Enhancing quantum technologies with classical AI applications

Innovation

• Quantum computing enhances AI solutions development
• Classical AI boosts quantum technologies research
• Hybrid software engineering optimizes energy consumption