Problem
Research questions and friction points this paper is trying to address.
Artificial Intelligence
Quantum Information
Quantum Machine Learning
Hybrid Quantum-Classical Systems
Cross-disciplinary Integration
When AI meets quantum information: A comprehensive review
🤖 AI Summary
This study addresses the lack of systematic synthesis in the interdisciplinary research between artificial intelligence (AI) and quantum information, where the mechanisms of mutual enhancement remain unclear. It presents the first comprehensive integration of advances in both AI-for-quantum—encompassing quantum algorithm discovery, hardware stabilization, and experimental automation—and quantum-for-AI, including quantum acceleration, neural network design, and tensor network representations. By unifying key methodologies from machine learning, quantum algorithms, quantum control, and hybrid quantum–classical systems, the work establishes a cohesive knowledge framework for their synergistic development. It further identifies core challenges centered on reproducibility, scalability, and hardware–software co-design, offering a clear roadmap to guide future convergence across theoretical, experimental, and engineering domains.
📝 Abstract
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI for quantum information
quantum-inspired AI
hybrid quantum-classical systems
quantum algorithm discovery
tensor-network representations
🔎 Similar Papers
No similar papers found.
