As classical computing approaches its physical limits, quantum computing offers exponential power, yet the current Noisy Intermediate-Scale Quantum (NISQ) era faces hardware constraints from high error rates, decoherence, and control challenges. This has catalyzed Quantum Artificial Intelligence (QAI), a synergistic, bidirectional field. This review analyzes the “AI for Quantum” framework, where AI solves quantum bottlenecks, and the “Quantum for AI” framework, where quantum computing creates new intelligent paradigms.
The “AI for Quantum” framework details AI's success in overcoming hardware bottlenecks. This includes machine learning for autonomous device characterization, calibration, and high-fidelity readout, plus the development of advanced AI-based decoders and hardware-adapted Quantum Error Correction codes. It also covers optimizing quantum compilation.
The “Quantum for AI” framework traces the evolution from early algorithms such as HHL and QSVM to dominant Variational Quantum Algorithms and Quantum Neural Networks. We critically analyze primary obstacles, including the barren plateau phenomenon and the exponential concentration of quantum kernels, along with their mitigation strategies. The review also covers advances in quantum optimization, such as Quantum Annealing and the Quantum Approximate Optimization Algorithm, and the emergence of advanced models like Quantum Natural Language Processing.
This bidirectional fusion is the key strategy for accelerating progress from the NISQ era toward fault-tolerant computation and developing next-generation, hybrid quantum-classical intelligent systems.