Integration of Quantum Computing with Artificial Intelligence: A Systematic Review
Abstract
Objective: To synthesize the state of the art on how quantum computing (QC) and artificial intelligence (AI) intersect, including algorithmic foundations, software/hardware stacks, empirical evidence of advantage, applications, and open challenges. Methods: We executed a structured search (2019 – 20th August 2025) across major journals (Nature portfolio, APS/PR, Elsevier), arXiv, and official framework documentation (Qiskit, Cirq/TFQ, PennyLane; AWS Braket; NVIDIA CUDA-Q). We included peer‑reviewed studies, comprehensive surveys, and official docs; we excluded opinion pieces and non-reproducible claims. Results: The field converges on hybrid quantum‑classical workflows with two dominant AI families: (i) quantum kernel methods and (ii) variational circuits/QNNs. Trainability remains a core challenge (barren plateaus and noise), with mitigation strategies advancing (local costs, layerwise training, engineered dissipation, learned decoders). Frameworks and cloud orchestrators now support end‑to‑end hybrid training. Empirical “utility” demonstrations in physics‑inspired tasks exist, while domain‑level advantage in mainstream ML is unproven and data‑dependent. Conclusions: QC‑AI integration is maturing into reproducible hybrid stacks. Near‑term value lies in quantum kernels with tailored feature maps, physics‑regularized QNNs, simulator‑accelerated pipelines, and AI‑for‑QC (calibration/decoding). Broad ML advantage awaits lower‑noise hardware and quantum‑friendly data embeddings.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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