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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