In the context of quantum-inspired machine learning, quantum state discrimination is a useful tool for classification problems. We implement a local approach combining the k-nearest neighbors algorithm with some quantum-inspired classifiers. We compare the performance with respect to well-known classifiers applied to benchmark datasets.
(2023). Quantum-Inspired Applications for Classification Problems [journal article - articolo]. In ENTROPY. Retrieved from https://hdl.handle.net/10446/239450
Quantum-Inspired Applications for Classification Problems
Bertini, Cesarino;Leporini, Roberto
2023-01-01
Abstract
In the context of quantum-inspired machine learning, quantum state discrimination is a useful tool for classification problems. We implement a local approach combining the k-nearest neighbors algorithm with some quantum-inspired classifiers. We compare the performance with respect to well-known classifiers applied to benchmark datasets.File allegato/i alla scheda:
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