QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
DOI:
https://doi.org/10.1609/aaai.v36i8.20807Keywords:
Machine Learning (ML)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.Downloads
Published
2022-06-28
How to Cite
Silver, D., Patel, T., & Tiwari, D. (2022). QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 8324-8332. https://doi.org/10.1609/aaai.v36i8.20807
Issue
Section
AAAI Technical Track on Machine Learning III