Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets.

ABSTRACT: The discovery of quantum algorithms offering provable advantages over the best known classical alternatives, together with the parallel ongoing revolution brought about by classical artificial intelligence, motivates a search for applications of quantum information processing methods to machine learning. Among several proposals in this domain, quantum kernel methods have emerged as particularly promising candidates. However, while some rigorous speedups on certain highly specific problems have been formally proven, only empirical proof-of-principle results have been reported so far for real-world datasets. Moreover, no systematic procedure is known, in general, to fine tune and optimize the performances of kernel-based quantum classification algorithms. At the same time, certain limitation

SUBMITTER: John M 

PROVIDER: S-EPMC10297005 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature