Model and solve optimal control problems in Julia
A Julia package for solving quantum optimal control problems using direct trajectory optimization.
Quantum Optimal Control with Direct Collocation
A high-performance library for gradient based quantum optimal control
quantum optimal control with direct collocation
Trajectory optimization (indirect with iLQR, direct with SQP), model predictive control, and additional tools for quantum optimal control.
Perform open-loop optimization of continuous control pulses using fast, high-order timestepping based on Hermite interpolation to find optimal control pulses for implementing quantum gates.
A julia package for doing quantum optimal control with the trajectory optimization algorithm ALTRO
We show that it is possible to obtain non-maximally entangled states with the use of the bosonic analog of XY Hamiltonian and the methods of quantum optimal control.
This repository applies Machine Learning to Quantum Optimal Control (QOC) for preparing the highly entangled Greenberger–Horne–Zeilinger (GHZ) state.
Add a description, image, and links to the quantum-optimal-control topic page so that developers can more easily learn about it.
To associate your repository with the quantum-optimal-control topic, visit your repo's landing page and select "manage topics."