quantum-optimal-control

Model and solve optimal control problems in Julia, both on CPU and GPU.

A Julia package for solving quantum optimal control problems using direct trajectory optimization.

Quantum Optimal Control with Direct Collocation

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 high-performance library for gradient based quantum optimal control

Simulate Qiskit circuits at the pulse level

quantum optimal control with direct collocation

Trajectory optimization (indirect with iLQR, direct with SQP), model predictive control, and additional tools for quantum optimal control.

This repository applies Machine Learning to Quantum Optimal Control (QOC) for preparing the highly entangled Greenberger–Horne–Zeilinger (GHZ) state.

CUDA-Q dynamics + classical optimization of the effective Mølmer-Sørensen interaction (χt = π/8) to prepare the two-qubit Bell state. Uses a typical trapped-ion MS coupling scale.

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.

A julia package for doing quantum optimal control with the trajectory optimization algorithm ALTRO

A differentiable model of a superconducting control line — DAC, filter, IQ mixer, amplifier — wired to a three-level transmon in JAX, with hand-derived adjoints and a gradient checked against finite differences. Finds a bandwidth threshold below which classical pre-distortion makes the gate worse. Tesseract Hackathon 2026, Track 03.