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