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

• URL: http://arxiv.org/abs/2309.15056v1
• Date: Tue, 26 Sep 2023 16:36:11 GMT
• Title: QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
• Authors: Daniel Silver, Tirthak Patel, Devesh Tiwari
• Abstract summary: Quilt is a framework for performing multi-class classification task on error-prone quantum computers. It demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.
• Score: 11.536317744969514
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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