Quantum Machine Learning with SQUID

• URL: http://arxiv.org/abs/2105.00098v3
• Date: Fri, 27 May 2022 11:39:10 GMT
• Title: Quantum Machine Learning with SQUID
• Authors: Alessandro Roggero, Jakub Filipek, Shih-Chieh Hsu, Nathan Wiebe
• Abstract summary: We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
• Score: 64.53556573827525
• License: http://creativecommons.org/licenses/by/4.0/
• 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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