Quantum ensembles of quantum classifiers
This work addresses the challenge of improving classification accuracy in quantum machine learning, potentially benefiting applications like automated image recognition and medical diagnosis, though it appears incremental as it extends classical ensemble methods to the quantum domain.
The paper tackles the problem of collective decision making in quantum machine learning by introducing quantum ensembles of quantum classifiers, enabling exponentially large ensembles without individual training and demonstrating new results for both quantum and classical machine learning.
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 of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which -- similar to Bayesian learning -- the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.