Quantum AI: Advancing Quantum Technologies with Artificial Intelligence

Artificial intelligence (AI) and quantum technologies represent two of the most transformative scientific frontiers of the 21st century. While quantum computing, sensing, and communication promise unprecedented advancements in information processing, control, and measurement, the inherent complexity and vast Hilbert spaces of quantum systems pose profound scientific and technical challenges. Increasingly, AI and machine learning methods are being leveraged to accelerate quantum research—optimizing device control, guiding the discovery of quantum algorithms and materials, mitigating errors, and interpreting experimental results. Recent studies highlight successful applications of AI in quantum circuit compilation, reinforcement learning for quantum control, and data-driven modeling of quantum hardware. Despite this progress, the full synergy between these disciplines remains only partly realized: critical questions persist around scalability, cross-domain applicability, standardization of benchmarks, and the interpretability and trustworthiness of AI-driven quantum solutions.This Research Topic seeks to bring together cutting-edge contributions at the intersection of artificial intelligence and quantum science and technology. Its aim is to advance understanding of how AI methods can accelerate the development, characterization, and operation of quantum systems, while helping to establish emerging best practices, shared frameworks, and reproducible methodologies for Quantum AI research. The issue also aims to highlight open challenges and identify high-impact opportunities where AI can most effectively contribute to progress across the quantum technology stack. Particular emphasis is placed on approaches that demonstrate measurable improvements in performance, scalability, robustness, interpretability, or scientific insight enabled by the integration of AI and quantum technologies. Key questions motivating this topic include: Which aspects of quantum system design, control, and experimentation are most amenable to AI-driven acceleration? What new learning architectures, models, and algorithms are emerging at the AI–quantum interface? To what extent can AI guide quantum device operation, error mitigation strategies, materials discovery, and the interpretation of complex quantum data?Contributions spanning fundamental theory, methodological innovation, open-source tools, and application-driven case studies are encouraged.Contributions to this Research Topic may span theoretical, computational, and experimental research, but are limited to the intersection of AI/ML and quantum technologies. We welcome studies that address both academic and industry-driven problems, provided quantum systems or concepts are central. To gather further insights in this rapidly evolving area, we welcome articles addressing, but not limited to, the following themes:• Machine learning for quantum algorithm discovery, circuit synthesis, compilation, and benchmarking• AI techniques for quantum error mitigation, error correction, device control, calibration, and noise characterization• Data-driven approaches to quantum many-body physics, quantum chemistry, and quantum materials• Applications of deep learning, reinforcement learning, and generative models in quantum experiments and system optimization• Quantum artificial intelligence, including quantum-enhanced machine learning and hybrid quantum–classical learning architectures• Trustworthy AI for quantum science, including interpretability, robustness, validation, and uncertainty quantification• Cross-disciplinary applications of AI in quantum computing, sensing, metrology, communications, and materials discovery• Case studies, reviews, and perspective articles highlighting emerging applications, practical deployments, and lessons learned• Datasets, open-source tools, benchmarks, and reproducibility frameworks for Quantum AI research• AI-driven discovery of quantum materials and devices, including superconductors, topological phases, and novel qubit platforms• AI for scalable classical simulation of quantum systems, including tensor networks and neural quantum states• Graph neural networks and geometric learning for quantum systems and many-body structuresWe welcome a broad range of article types including Original Research, Reviews, Mini-Reviews, Methods, Perspectives, Brief Research Reports, and Data Reports. Topic editors: Utpal Roy Source: https://www.frontiersin.org/research-topics/79446/quantum-ai-advancing-quantum-technologies-with-artificial-intelligence