MicroAlgo, under the ticker MLGO, has unveiled a groundbreaking quantum entanglement-based training algorithm designed to enhance supervised quantum classifiers. This cutting-edge development, termed the Entanglement-Assisted Training Algorithm, introduces a novel cost function derived from Bell inequalities. This function allows for the concurrent encoding of errors across multiple training samples, effectively breaking through the performance constraints of conventional algorithms.
By employing quantum entanglement, the algorithm enables simultaneous processing of multiple training samples and their labels, pushing the boundaries of efficiency and applicability for supervised quantum classifiers. This innovation signifies a substantial advancement in quantum computing, potentially transforming the landscape of algorithm training and deployment.
