Differentiable Analog Quantum Computing for Optimization and Control
Abstract
We formulate the first differentiable analog quantum computing framework with specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by {\em orders of magnitude}.
Cite
Text
Leng et al. "Differentiable Analog Quantum Computing for Optimization and Control." Neural Information Processing Systems, 2022.Markdown
[Leng et al. "Differentiable Analog Quantum Computing for Optimization and Control." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/leng2022neurips-differentiable/)BibTeX
@inproceedings{leng2022neurips-differentiable,
title = {{Differentiable Analog Quantum Computing for Optimization and Control}},
author = {Leng, Jiaqi and Peng, Yuxiang and Qiao, Yi-Ling and Lin, Ming and Wu, Xiaodi},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/leng2022neurips-differentiable/}
}