In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.
@article{arxiv.2105.00098,
title = {Quantum Machine Learning with SQUID},
author = {Alessandro Roggero and Jakub Filipek and Shih-Chieh Hsu and Nathan Wiebe},
journal= {arXiv preprint arXiv:2105.00098},
year = {2022}
}