Quantum Machine Learning with SQUID

Alessandro Roggero; Jakub Filipek; Shih-Chieh Hsu; Nathan Wiebe

doi:10.22331/q-2022-05-30-727

Quantum Machine Learning with SQUID

Alessandro Roggero^1,2, Jakub Filipek³, Shih-Chieh Hsu⁴, and Nathan Wiebe^5,6,4

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

Published:	2022-05-30, volume 6, page 727
Eprint:	arXiv:2105.00098v3
Doi:	https://doi.org/10.22331/q-2022-05-30-727
Citation:	Quantum 6, 727 (2022).

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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.

Featured image: Pictorial representation of the hybrid classifier model used for MNIST classification. Panel $(A)$ shows the complete network including an encoder with $M_0$ units and a decoder with $M_1$ units. Panel $(B)$ shows the implemented classical classifier composed by two units and panel $(C)$ shows a schematic of the quantum models: input parameters coming from the encoder determine the unitary $W$ while the output is obtained upon measurement of the qubits.

► BibTeX data

@article{Roggero2022quantummachine,
  doi = {10.22331/q-2022-05-30-727},
  url = {https://doi.org/10.22331/q-2022-05-30-727},
  title = {Quantum {M}achine {L}earning with {SQUID}},
  author = {Roggero, Alessandro and Filipek, Jakub and Hsu, Shih-Chieh and Wiebe, Nathan},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {6},
  pages = {727},
  month = may,
  year = {2022}
}

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Cited by

[1] Natalie Klco, Alessandro Roggero, and Martin J. Savage, "Standard model physics and the digital quantum revolution: thoughts about the interface", Reports on Progress in Physics 85 6, 064301 (2022).

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