Quantum Version

Research into "quantum versions" of classical algorithms and machine learning models aims to leverage quantum computing's potential for speedups and enhanced capabilities. Current efforts focus on adapting established methods like reinforcement learning, federated learning, and k-nearest neighbors to quantum platforms, often employing variational quantum ansatzes or quantum annealing, and exploring the use of quantum-enhanced heuristics. These investigations are revealing both the advantages and limitations of quantum approaches, particularly concerning the trade-offs between improved performance and the resource constraints of near-term quantum devices, ultimately informing the development of practical quantum algorithms for data science and machine learning tasks.