Quantum Machine Learning with SQUID

Alessandro Roggero1,2, Jakub Filipek3, Shih-Chieh Hsu4, and Nathan Wiebe5,6,4

1Institute for Nuclear Theory, University of Washington, Seattle, WA 98195, USA
2InQubator for Quantum Simulation (IQuS), Department of Physics, University of Washington, Seattle, WA 98195, USA
3Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
4Department of Physics, University of Washington, Seattle 98195, USA
5University of Toronto, Department of Computer Science, Toronto, ON M5G 1V7, Canada
6Pacific Northwest National Laboratory, Richland, WA 99352, USA

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Abstract

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.

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