BOULDER OPAL
Step-by-step how-to guides for the features in BOULDER OPAL.
Getting started with Q-CTRL BOULDER OPAL
View previously run actions in BOULDER OPAL
Represent quantum systems for optimization, simulation, and other tasks using graphs
Create computational graphs for computations in the Q-CTRL Python package
Use pulses from an open-source library in Q-CTRL calculations
Incorporate QuTiP objects and programming syntax directly into graphs
Highly-configurable non-linear optimization framework for quantum control
Incorporate time symmetry into optimized waveforms
Incorporate smoothing of optimized waveforms
Incorporate nonlinear Hamiltonian dependences on control signals
Create optimized controls from a user-defined set of basis functions
Efficiently perform control optimization on sparse Hamiltonians
Design controls that are robust against strong time-dependent noise sources with stochastic optimization
Efficient state preparation using Mølmer–Sørensen-type interactions with in-built convenience functions
Calculate the Mølmer–Sørensen gate evolution characteristics for trapped ions
Simulate the dynamics of closed quantum systems
Simulate the dynamics of closed quantum systems in the presence of Non-Markovian noise
Evaluate the performance of multi-qubit circuits with and without noise
Calculating the dynamics of a quantum system described by a GKS–Lindblad master equation
Calculate the dynamics of a high-dimensional quantum system described by a GKS–Lindblad master equation
Characterize the robustness of a control pulse to quasi-static noise
Calculate the frequency-domain noise sensitivity of driven controls
Calibrate RF control channels for maximum pulse performance
Closed-loop optimization without complete system models
Use external data management package for simple closed-loop optimizations
Design waveforms using a model-independent reinforcement learning framework
Reconstructing noise spectra using shaped control pulses
Estimate Hamiltonian model parameters using measured data and the graph-based optimization engine
Characterize transmission-line bandwidth via probe measurements and the graph-based optimization engine
