# Branching quantum convolutional neural networks

@article{MacCormack2022BranchingQC, title={Branching quantum convolutional neural networks}, author={Ian MacCormack and Conor Delaney and Alexey Galda and Nidhi Aggarwal and Prineha Narang}, journal={Physical Review Research}, year={2022} }

Ian MacCormack, 2, 3 Conor Delaney, Alexey Galda, 3 Nidhi Aggarwal, and Prineha Narang ∗ Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, Illinois 60637, USA Department of Physics, Princeton University, Princeton, New Jersey 08544, USA Aliro Technologies, Inc. Boston, Massachusetts 02135, USA James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts…

## 3 Citations

Absence of Barren Plateaus in Quantum Convolutional Neural Networks

- Computer SciencePhysical Review X
- 2021

This work rigorously analyze the gradient scaling for the parameters in the QCNN architecture and finds that the variance of the gradient vanishes no faster than polynomially, implying that QCNNs do not exhibit barren plateaus.

RGB Image Classification with Quantum Convolutional Ansaetze

- Computer ScienceQuantum Inf. Process.
- 2022

This is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs, and it is demonstrated that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.

Fast suppression of classification error in variational quantum circuits

- Physics
- 2021

Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not…

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This work rigorously analyze the gradient scaling for the parameters in the QCNN architecture and finds that the variance of the gradient vanishes no faster than polynomially, implying that QCNNs do not exhibit barren plateaus.

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