Operator Learning with Variational Quantum Circuits
Abstract
A new methodology is developed for quantum machine learning which enables variational quantum circuits to learn linear and non-linear solution operators to differential equations. This is devised by extending the framework of the DeepONet, a technique for modeling differential equations. The branch and trunk neural networks are replaced by variational quantum circuits, leveraging the quantum universal approximation theorem. The methodology is tested on several differential equations and yields low approximation & generalization errors even with shallow circuit depths. In addition, it does not encounter barren plateaus during training. The model displays improved error and parameter scaling relative to the classical DeepONet. This is due to the intrinsic property that the approximation error depends on the dimension of the input function space, rather than the number of sensor locations. A theoretical framework is established in the form of explicit error bounds, which measure the contributions of the truncation, branch, and trunk circuit errors to the total operator approximation error. Full derivations for the error bounds are developed in the norms L2, Lp, C0, and Sobolev Hk, along with a classical-quantum comparative analysis (as Supplemental Materials).
Related
- Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
- BAHAMAS: A Control Plane for Optimization and Execution of Variational Quantum Circuits
- Subspace Controllability in Variational Quantum Circuits: Maximising Expressivity and Increasing Search Efficiency with Dynamical Lie Algebras
- A quantum representation of π fragmentation functions through variational quantum circuits
- Data informed Ansatz Selection for Variational Quantum Circuits
BibTeX
@misc{wu2026operator,
title = {{Operator Learning with Variational Quantum Circuits}},
author = {Jorgen Wu and Masoud Barati and Peyman Givi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14131},
eprint = {2609.14131},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14131},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Operator Learning with Variational Quantum Circuits
AU - Jorgen Wu
AU - Masoud Barati
AU - Peyman Givi
PY - 2026
DA - 2026/09/12
PB - arXiv
DO - 10.48550/arxiv.2609.14131
UR - https://arxiv.org/abs/2609.14131
AB - A new methodology is developed for quantum machine learning which enables variational quantum circuits to learn linear and non-linear solution operators to differential equations. This is devised by extending the framework of the DeepONet, a technique for modeling differential equations. The branch and trunk neural networks are replaced by variational quantum circuits, leveraging the quantum universal approximation theorem. The methodology is tested on several differential equations and yields low approximation & generalization errors even with shallow circuit depths. In addition, it does not encounter barren plateaus during training. The model displays improved error and parameter scaling relative to the classical DeepONet. This is due to the intrinsic property that the approximation error depends on the dimension of the input function space, rather than the number of sensor locations. A theoretical framework is established in the form of explicit error bounds, which measure the contributions of the truncation, branch, and trunk circuit errors to the total operator approximation error. Full derivations for the error bounds are developed in the norms L^2, L^p, C^0, and Sobolev H^k, along with a classical-quantum comparative analysis (as Supplemental Materials).
ER -