White Paper Explores Intersection of Quantum Computing and AI, Maps Research Agenda for Hybrid Technologies
Key Takeaways
- ▸Quantum computing and AI represent complementary technologies with significant synergistic potential, requiring coordinated research efforts to unlock mutual benefits
- ▸Energy optimization and resource estimation are critical challenges in developing practical quantum-classical hybrid AI systems
- ▸Alignment between quantum AI research and quantum hardware development roadmaps is essential for achieving viable real-world applications
Summary
A comprehensive white paper has been submitted to arXiv examining the convergence of quantum computing and artificial intelligence, exploring how these two transformative technologies can mutually advance one another. The research, authored by rramadass and last revised in June 2025, investigates dual pathways: how quantum computing can accelerate AI development and innovation, and conversely, how classical AI techniques can enhance quantum technology research and development—particularly in quantum computing and quantum sensing applications.
The white paper establishes a long-term research agenda focused on foundational questions about AI-quantum computing interactions. It identifies critical challenges including aligning quantum AI developments with quantum hardware roadmaps, optimizing resource allocation with emphasis on energy consumption reduction, and advancing the emerging discipline of hybrid quantum-classical software engineering. The authors conclude with recommendations for orchestrating theoretical work, enhancing European industrial competitiveness, and addressing broader societal implications of these converging technologies.
- The hybrid quantum-classical software engineering discipline requires new methodologies and frameworks still under development
Editorial Opinion
This white paper addresses a crucial frontier in computing research by systematically examining how quantum and AI technologies can strengthen each other. Rather than treating these as separate domains, the authors' focus on intersection points and mutual enhancement suggests a more pragmatic approach to next-generation computing. However, the emphasis on challenges like energy consumption and resource optimization hints at the significant engineering obstacles that remain before these theoretical synergies translate into practical, scalable applications.
