QUILT: Effective Multi-Class Classification on Quantum Computers Using
  an Ensemble of Diverse Quantum Classifiers
Daniel Silver Tirthak Patel Devesh Tiwari
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.