Optimal Control for Cancer Chemotherapy Using Hybrid Quantum Particle Swarm Optimization
Abstract
Optimal control in cancer chemotherapy is challenged by tumor heterogeneity and mutations, which complicate treatment effectiveness. Traditional methods, such as Pontryagin's maximum principle (PMP), are often hindered by their reliance on an initial guess for the costate equation, affecting accuracy and convergence. To address these limitations, this work introduces a hybrid quantum particle swarm optimization (QPSO) method based on regularization. QPSO is employed for global exploration to approximate the optimal control trajectory, followed by a regularization-based refinement to ensure smoothness and consistency with optimality conditions. The Hamiltonian function is used for first- and second-order optimality checks, verifying solution quality. Numerical case studies explore various drug effectiveness functions, demonstrating the role of periodic and localized drug delivery in achieving robust tumor suppression and providing insights into the impact of different drug combinations on optimal chemotherapy strategies.
Versions
Related
- Optimal Control of State Constrained Systems via Measure Relaxations and Polynomial Optimization
- A Stochastic Optimization Approach to Control-Affine Optimal Control Problems
- A New Perspective on Optimal Power Flow: From Control Theory to Numerical Optimization
- Balanced Active Subspaces for Optimal Control via Trajectory Optimization
- Measurement-angle control of a continuously monitored quantum oscillator: purification limits and optimal protocols
BibTeX
@misc{kidane2026optimal,
title = {{Optimal Control for Cancer Chemotherapy Using Hybrid Quantum Particle Swarm Optimization}},
author = {Bereket Sitotaw Kidane and Md Samiul Haque Motayed and Shuo Wang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11197},
eprint = {2609.11197},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11197},
}
RIS
TY - UNPB
TI - Optimal Control for Cancer Chemotherapy Using Hybrid Quantum Particle Swarm Optimization
AU - Bereket Sitotaw Kidane
AU - Md Samiul Haque Motayed
AU - Shuo Wang
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.11197
UR - https://arxiv.org/abs/2609.11197
AB - Optimal control in cancer chemotherapy is challenged by tumor heterogeneity and mutations, which complicate treatment effectiveness. Traditional methods, such as Pontryagin's maximum principle (PMP), are often hindered by their reliance on an initial guess for the costate equation, affecting accuracy and convergence. To address these limitations, this work introduces a hybrid quantum particle swarm optimization (QPSO) method based on regularization. QPSO is employed for global exploration to approximate the optimal control trajectory, followed by a regularization-based refinement to ensure smoothness and consistency with optimality conditions. The Hamiltonian function is used for first- and second-order optimality checks, verifying solution quality. Numerical case studies explore various drug effectiveness functions, demonstrating the role of periodic and localized drug delivery in achieving robust tumor suppression and providing insights into the impact of different drug combinations on optimal chemotherapy strategies.
ER -