QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

Daniel Silver; Tirthak Patel; Devesh Tiwari

AAAI 2022technical28 citations

QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

Daniel Silver, Tirthak Patel, Devesh Tiwari

Abstract

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. QUILT is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. QUILT is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise free. QUILT demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.

BibTeX

@inproceedings{aaai2022_quilteffectivemu,
  title = {QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers},
  author = {Daniel Silver and Tirthak Patel and Devesh Tiwari},
  booktitle = {AAAI 2022},
  year = {2022}
}