Quantum ensembles of quantum classifiers
Abstract
Quantum machine learning witnesses an increasing amount of quantum algorithms for datadriven 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 singlequbit 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.
 Publication:

Scientific Reports
 Pub Date:
 February 2018
 DOI:
 10.1038/s41598018204033
 arXiv:
 arXiv:1704.02146
 Bibcode:
 2018NatSR...8.2772S
 Keywords:

 Quantum Physics;
 Computer Science  Machine Learning;
 Mathematics  Statistics Theory
 EPrint:
 19 pages, 9 figures