Quantum Machine Learning with SQUID

Understand

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.