EMERGING INTERDISCIPLINARY FIELDS IN COMPUTING: CONVERGENCE OF QUANTUM COMPUTING AND AI
Keywords:
quantum computing, quantum machine learning, variational quantum algorithms, hybrid quantum-classical systems, quantum kernels, optimizationAbstract
The convergence of quantumcomputing and artificial intelligence (AI) isemerging as a high-impact interdisciplinaryfield capable of reshaping computationalparadigms for optimization, simulation,learning, and scientific discovery. Whilenear-term quantum devices remain noisy andresource-constrained,hybridquantumclassical workflows already enable practicalexperimentation inmachine learning (ML),particularly through variational circuits,quantum kernels, and quantum-inspiredoptimization strategies. In parallel, AI isaccelerating quantum research by improvingcontrol,calibration,errormitigation,compilation, and materials discovery forhardwaredevelopment.Thisarticlesynthesizes foundational concepts, explainsthebidirectionalrelationshipbetween
quantum computing and AI, and reviewsleading application areas including quantummachinelearning,quantum-enhancedoptimization, generative modeling, andquantum-assisted scientific workflows. Wealso analyze technical bottlenecks—noise,scalability, data loading, interpretability,and benchmarking—and discuss evaluationstrategies and governance considerations.Finally, the paper proposes future researchdirections that emphasize reproducibility,standardized benchmarks, and domainfocused demonstrations where quantum–AIsynergy can outperform or complementclassical approaches..
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