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.

翻译：量子计算机理论上相比经典计算机具有显著加速能力；但近期的量子计算时代因量子比特数量少且易出错而受限。QUILT框架专为在当前易出错的量子计算机上高效执行多类分类任务而设计。该框架在实际量子计算机及未来噪声水平降低的量子机器投影噪声条件下均进行了评估。结果表明，在五量子比特系统上，QUILT对MNIST数据集实现了高达85%的多类分类准确率。