Design model-based controls

Generate and test robust controls in Boulder Opal

Highly-configurable non-linear optimization framework for quantum control

Incorporate nonlinear Hamiltonian dependences on control signals

Efficiently perform control optimization on sparse Hamiltonians

Design controls that are robust against strong time-dependent noise sources with stochastic optimization

Incorporate smoothing of optimized waveforms

Perform graph-based optimizations when gradients are costly

Incorporate time symmetry into optimized waveforms

Optimizing over the duration of your controls

Create optimized controls from superpositions of basis functions

Incorporate robustness into the design of optimal pulses

Design pulses that minimize leakage to unwanted states

Defining parameters of the optimization using the cost history and early halt conditions

Configuring the stochastic optimizer by requesting the cost history from the optimization results

Characterize the robustness of a control pulse to quasi-static noise

Calculate the frequency-domain noise sensitivity of driven controls