Supervised learning with quantum-enhanced feature spaces
Name
Harrow_EMB UNTIL DSept 13, 2019_orig man, arXiv_Supervised learning with quantum-enhanced feature spaces.pdf
Description
Submitted version
Size
1.23 MB
Format
Unknown
Checksum (MD5)
c4b8dbbfc65a7e0ad3f766dbb9834382
Author(s)
Havlíček, Vojtěch
Córcoles, Antonio D
Temme, Kristan
Harrow, Aram W.
Kandala, Abhinav
Chow, Jerry M
Gambetta, Jay M
Date Issued
2019
Journal
Nature
Publisher
Springer Science and Business Media LLC
Version
Original manuscript
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 circuit 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 computers to machine learning. 1,2 3
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41586-019-0980-2
