Supervised learning with quantum-enhanced feature spaces

Vojtěch Havlíček, Antonio D. Córcoles (), Kristan Temme (), Aram W. Harrow, Abhinav Kandala, Jerry M. Chow and Jay M. Gambetta
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Vojtěch Havlíček: IBM T. J. Watson Research Center
Antonio D. Córcoles: IBM T. J. Watson Research Center
Kristan Temme: IBM T. J. Watson Research Center
Aram W. Harrow: Massachusetts Institute of Technology
Abhinav Kandala: IBM T. J. Watson Research Center
Jerry M. Chow: IBM T. J. Watson Research Center
Jay M. Gambetta: IBM T. J. Watson Research Center

Nature, 2019, vol. 567, issue 7747, 209-212

Abstract: Abstract Machine learning and quantum computing are two technologies that each have the potential to alter how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous in pattern recognition, with support vector machines (SVMs) being the best known method for classification problems. However, there are limitations to the successful solution to such classification problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element in the computational speed-ups enabled by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here we propose and experimentally implement two quantum algorithms on a superconducting processor. A key component in both methods is the use of the quantum state space as feature space. The use of a quantum-enhanced feature space that is only efficiently accessible on a quantum computer provides a possible path to quantum advantage. The algorithms solve a problem of supervised learning: the construction of a classifier. One method, the quantum variational classifier, uses a variational quantum circuit1,2 to classify the data in a way similar to the method of conventional SVMs. The other method, a quantum kernel estimator, estimates the kernel function on the quantum computer and optimizes a classical SVM. The two methods provide tools for exploring the applications of noisy intermediate-scale quantum computers3 to machine learning.

Date: 2019
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Citations: View citations in EconPapers (15)

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DOI: 10.1038/s41586-019-0980-2

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