Quantum Support Vector Machine for Big Data Classification
Abstract
Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data innerproduct (kernel) matrix.
 Publication:

Physical Review Letters
 Pub Date:
 September 2014
 DOI:
 10.1103/PhysRevLett.113.130503
 arXiv:
 arXiv:1307.0471
 Bibcode:
 2014PhRvL.113m0503R
 Keywords:

 03.67.Ac;
 07.05.Mh;
 Quantum algorithms protocols and simulations;
 Neural networks fuzzy logic artificial intelligence;
 Quantum Physics;
 Computer Science  Machine Learning
 EPrint:
 5 pages