User guides

Upgrade your pre-existing Boulder Opal code by the end of 2023

Monitor job status and retrieve results from previously run calculations in Boulder Opal

Change the verbosity of the messaging

Cite relevant Boulder Opal articles and specific documentation pages

Authenticate your Boulder Opal session without a browser

Create graphs for computations with Boulder Opal

Represent quantum systems for optimization, simulation, and other tasks using graphs

Use predefined signals from Boulder Opal

Use analytical expressions to construct your Hamiltonian

Perform calculations using optimization results in a single graph

Reapply graph nodes for multiple applications

Perform graph-based optimizations when gradients are costly

Highly-configurable non-linear optimization framework for quantum control

Incorporate nonlinear Hamiltonian dependences on control signals

Efficiently perform control optimization on sparse Hamiltonians

Incorporate robustness into the design of optimal pulses

Design pulses that minimize leakage to unwanted states

Efficient state preparation using Mølmer–Sørensen-type interactions

Creating efficient gates for trapped ions without individually addressing the ions

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

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

Incorporate smoothing of optimized waveforms

Incorporate time symmetry into optimized waveforms

Optimizing over the duration of your controls

Create optimized controls from superpositions of basis functions

Reconstructing noise spectra using shaped control pulses

Estimate Hamiltonian model parameters using measured data and the graph-based optimization engine

Estimate Hamiltonian model parameters using measured data and the graph-based stochastic optimization engine

Characterize transmission-line bandwidth via probe measurements and the graph-based optimization engine

Calibrate RF control channels for maximum pulse performance

Closed-loop optimization without complete system models

Automate your calibration workflows with the Q-CTRL Experiment Scheduler

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

Compute the long time limit density matrix of Lindblad dynamics from a time-independent generator

Calculate the Mølmer–Sørensen gate evolution characteristics for trapped ions

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

Calculate the frequency-domain noise sensitivity of driven controls

Prepare optimized controls for hardware implementation

Use pulses from an open-source library in Boulder Opal calculations

Incorporate QuTiP objects and programming syntax directly into graphs

Integrate Boulder Opal pulses directly into Quantum Machines hardware using the Q-CTRL QUA Adapter

Use external data management package for simple closed-loop optimizations