Physics Research Conference

Machine learning algorithms look for patterns in data. Frequently, that data comes in the form of large arrays of high-dimensional vectors. Quantum computers are adept at manipulating large arrays of high-dimensional vectors. This talk presents a series of quantum algorithms for big data analysis. The ability of quantum computers to perform Fourier transforms, find eigenvectors and eigenvalues, and invert matrices translates into quantum algorithms for clustering, principal component analysis, and for identifying topological features such as numbers of connected components, holes and voids. These quantum algorithms are exponentially faster than their classical counterparts: complex patterns in datasets of size N can be identified in time O( log N). The talk will discuss methods for implementing quantum machine learning algorithms on the current generation of quantum information processors.