Quantum principal component analysis
Abstract
The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyse the results statistically. For nonsparse but lowrank quantum states, revealing eigenvectors and corresponding eigenvalues in classical form scales superlinearly with the system dimension. Here we show that multiple copies of a quantum system with density matrix ρ can be used to construct the unitary transformation e^{iρt}. As a result, one can perform quantum principal component analysis of an unknown lowrank density matrix, revealing in quantum form the eigenvectors corresponding to the large eigenvalues in time exponentially faster than any existing algorithm. We discuss applications to data analysis, process tomography and state discrimination.
 Publication:

Nature Physics
 Pub Date:
 September 2014
 DOI:
 10.1038/nphys3029
 arXiv:
 arXiv:1307.0401
 Bibcode:
 2014NatPh..10..631L
 Keywords:

 Quantum Physics
 EPrint:
 9 pages, Plain TeX