  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.
