AI & ML
Structure-preserving full- and low-rank exponential methods for the optimal control of Lindblad equations
arXiv cs.NA0 views
arXiv:2610.09403v1 Announce Type: new Abstract: Optimal control of open quantum systems governed by the Lindblad master equation requires the repeated solution of forward and adjoint evolution equations. For large Hilbert spaces, these computations demand numerical methods that are structure-preserving, accurate, and computationally efficient, while remaining compatible with nonsmooth optimization techniques. In this work, a unified framework for optimal control of Lindblad equations is presented. The continuous optimality system is derived from the Pontryagin maximum principle and solved by a sequential quadratic Hamiltonian (SQH) method. Its numerical realization is based on second-order exponential midpoint propagators for the forward and adjoint Lindblad equations with time-dependent Hamiltonians. Full-rank schemes preserving the Hermitian and positive-semidefinite structure of the density matrix are developed together with low-rank formulations that substantially reduce storage requirements and computational cost. Rigorous error estimates are established for the full- and low-rank forward and adjoint propagators. Numerical experiments confirm the predicted convergence rates, demonstrate the effectiveness of the low-rank approximations, and illustrate the performance of the proposed FREM-SQH and LREM-SQH algorithms for optimal control problems with smooth and nonsmooth control costs.
Source:https://arxiv.org/abs/2610.09403PreviousFully Deterministic Shallow Neural Networks with Localized Activations. Part II: Condition Number EstimatesNextLeapfrog Crank--Nicolson decoupling of wave--heat-type problems
Related posts

10 Open-Source Robotics Repositories for 3D Perception, VLA Policies, and Sensor Calibration
0dev.to
My self-modifying system tried to wire a gate-passing module into its own protection enforcer. It was refused three times.
0dev.to
Last night an AI agent performed brain surgery on a mouse
0Hacker News
The Card-Update Page Gets One Visit. Design for That Visit.
0dev.to UX
SaaS Partner Scout: A Script for Identifying GitHub SaaS Project Contributors
0dev.to GitHub
WARN Act and Retaliation — Can Your Employer Punish You for Asking About a Layoff Notice?
0dev.to Data