Quantum ensembles of quantum classifiers

摘要: Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many those are implementations classifiers, or models the classification data inputs computer. Following success collective making ensembles in classical learning, this paper introduces concept classifiers. Creating ensemble corresponds state preparation routine, after which classifiers evaluated parallel and their combined is accessed by single-qubit measurement. This framework naturally allows exponentially large -- similar Bayesian individual do not have be trained. As example, we analyse each classifier weighed according its performance classifying training data, leading new results as well learning.