Variational Quantum Optimization of Noisy Polarization Qubit Experiments
Abstract
Quantum networking enables advanced communication protocols, distributed quantum computing, and enhanced sensing. These applications are often hindered by current realizations of quantum network nodes, which utilize noisy and imperfect hardware. Here, we apply variational optimization to counteract the effects of such imperfections. We parameterize network protocols for use with variational methods and then use these methods to optimize simple prepare-and-measure protocols on an optical testbed as a proof-of-principle demonstration. We demonstrate automated polarization compensation, achieving results comparable to conventional methods. We also analyze the effects of hardware imperfections and dark counts on the performance of our optimizations. We compare the performance of various gradient estimation methods and propose natural extensions, including further improvements through the use of global optimization and hybrid gradient estimation methods, as well as the application of variational optimization to nodes deployed on fiber networks.
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BibTeX
@misc{javid2026variational,
title = {{Variational Quantum Optimization of Noisy Polarization Qubit Experiments}},
author = {Timur Javid and Brian Doolittle and Colin P. Lualdi and Brittany Conner and Michael Vayninger and Eric Chitambar and Paul G. Kwiat},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33290},
eprint = {2610.33290},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33290},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Variational Quantum Optimization of Noisy Polarization Qubit Experiments
AU - Timur Javid
AU - Brian Doolittle
AU - Colin P. Lualdi
AU - Brittany Conner
AU - Michael Vayninger
AU - Eric Chitambar
AU - Paul G. Kwiat
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.33290
UR - https://arxiv.org/abs/2610.33290
AB - Quantum networking enables advanced communication protocols, distributed quantum computing, and enhanced sensing. These applications are often hindered by current realizations of quantum network nodes, which utilize noisy and imperfect hardware. Here, we apply variational optimization to counteract the effects of such imperfections. We parameterize network protocols for use with variational methods and then use these methods to optimize simple prepare-and-measure protocols on an optical testbed as a proof-of-principle demonstration. We demonstrate automated polarization compensation, achieving results comparable to conventional methods. We also analyze the effects of hardware imperfections and dark counts on the performance of our optimizations. We compare the performance of various gradient estimation methods and propose natural extensions, including further improvements through the use of global optimization and hybrid gradient estimation methods, as well as the application of variational optimization to nodes deployed on fiber networks.
ER -