Quantum-inspired classical algorithms for principal component analysis and supervised clustering.
Year: 2018
Type: preprint
Abstract: We describe classical analogues to Lloyd et al.'s quantum algorithms for principal component analysis and nearest-centroid clustering. We introduce a classical algorithm model that assumes we can efficiently perform $\ell^2$-norm samples of input data, a natural analogue to quantum algorithms assuming efficient state preparation. In this model, our classical algorithms run in time polylogarithmic in input size, matching the runtime of the quantum algorithms with only polynomial slowdown. These algorithms indicate that their corresponding problems do not yield exponential quantum speedups.
Source: arXiv (Cornell University)
Author: Ewin Tang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2898913178