Researchers map 119 studies of AI and quantum optimization

A review of 119 studies reveals a rapidly growing intersection between artificial intelligence and quantum computing aimed at solving complex optimization problems. Researchers identified a detailed analysis of 67 papers, focusing on how machine learning can both assist and be assisted by quantum workflows, an area they term “adaptive” optimization. Learning reduces quantum evaluations, improves initialization, supports decomposition and penalty control, mitigates noise, and informs execution choices, whereas evidence that quantum computation improves learned optimizers remains largely confined to small-scale simulation. This suggests a key asymmetry in the field as researchers explore how to best allocate classical and quantum resources across the optimization process. Current approaches to quantum combinatorial optimization face limitations imposed by qubit availability, circuit reliability and the costs associated with sampling and calibration, prompting a shift toward adaptable workflows. Rather than relying on static quantum algorithms, researchers are increasingly exploring machine learning to dynamically configure optimization processes, a trend reflected in the growing body of work examining decisions previously fixed in…