Toolkit

Run your first calculation with Boulder Opal Toolkit

Learn which documentation articles will get you up to speed with using Boulder Opal

Get started with simulations and robust control optimization for theorists in Boulder Opal

Get started with hardware characterization and closed-loop optimization for experimentalists in Boulder Opal

Get the accounts and development tools required to run Boulder Opal

Understand how to use the different types of Boulder Opal documentation

An introduction to the capabilities and benefits of Boulder Opal

An introduction to the application of Boulder Opal for key tasks in quantum computing

An introduction to the application of Boulder Opal for augmenting the performance of quantum sensors in real environments

Understand how and when to integrate Boulder Opal into your research: for theorists or experimentalists, new hardware, or established systems

An overview of choices and tradeoffs in control design for your quantum system

Evaluating Boulder Opal performance across multiple benchmarks

Cite relevant Boulder Opal articles and specific documentation pages

An introduction to the purpose and functionality of the Q-CTRL Visualizer

Authenticate your Boulder Opal session without a browser

Learn how to submit, cancel, and track the progress of your calculations

Understanding the Boulder Opal computing environment and utilizing the compute resources effectively

Change the verbosity of the messaging

An overview of how Boulder Opal uses computational graphs to represent systems and perform operations

Tips and tricks to speed up your calculations in Boulder Opal

Approaches to handle multidimensional data efficiently in graphs

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

Perform calculations using optimization results in a single graph

Reapply graph nodes for multiple applications

An overview of how time-dependent functions are represented in Boulder Opal graphs

Create parameterized signals for simulation and optimization

Understand Boulder Opal graphs and nodes by smoothing a piecewise-constant pulse

Use predefined signals from Boulder Opal

Use analytical expressions to construct your Hamiltonian

An overview of the choices and tradeoffs in deciding how to run a simulation

Simulate and visualize quantum system dynamics in Boulder Opal

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

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

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

Calculate the frequency-domain noise sensitivity of driven controls

Generate and test robust controls in Boulder Opal

Incorporate robustness into the design of optimal pulses

Design pulses that minimize leakage to unwanted states

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

Build a system model using probe measurements and data fusion routines

Performing system identification with Boulder Opal

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

An overview of how Boulder Opal's AI tools can be used to automate the tune-up and optimization of quantum hardware systems

Use closed-loop optimization without a complete system model

Calibrate RF control channels for maximum pulse performance

Closed-loop optimization without complete system models

Create parameterized signals for closed-loop optimization

Engineering and simulating gates in superconducting transmon-cavity systems

Increasing robustness against dephasing and control noise using Boulder Opal pulses

Increasing robustness against control noise using Boulder Opal pulses

Increasing robustness against crosstalk in a two-qubit entangling operation

Hamiltonian-agnostic rapid tune-up of an arbitrary unitary on a qutrit

Engineering fast, leakage-free gates in superconducting cavity-qubit systems

Engineering fast cavity state generation in superconducting cavity-qubit systems

Robust $ZZ_\Theta$ gate with a transmon ancilla engineered using Boulder Opal

Generating single flux quantum gates robust to leakage and frequency drift

Reconstructing noise power spectrum density in transmon qubits using dynamical decoupling sequences

Creating optimal operations with the trapped ions module

Calculate the Mølmer–Sørensen gate evolution characteristics for trapped ions

Creating efficient gates for trapped ions without individually addressing the ions

Efficient state preparation using Mølmer–Sørensen-type interactions

Obtaining control solutions for parallel and specifiable multi-qubit gates using Boulder Opal pulses

Obtaining control solutions for two-qubit gates with modulation of the confining potential

Generating high-fidelity GHZ states using Boulder Opal pulses

Using Boulder Opal to improve two-qubit controlled-Z gates for cold atoms

Deployment of Boulder Opal optimal pulses to increase the fidelity of state preparation in a cold atom cloud quantum computer hardware

Using Boulder Opal to model and mitigate crosstalk in dense qubit arrays caused by non-linear response to control signals

Increasing robustness to charge noise in 2D quantum dot arrays driven by exchange interaction

Increasing detection area by $>10\times$ using $\pi$ pulses robust to field inhomogeneities across large diamond chips

Using Boulder Opal spectrum reconstruction tools to perform provably optimal leakage-free sensing with spectrally concentrated Slepian pulses

Using Boulder Opal robust Raman pulses to boost fringe contrast in tight-SWAP cold atom interferometers by an order of magnitude

Export Boulder Opal pulses to use in real hardware

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

The Python interface to Boulder Opal's various features

Visualize common quantum control objects

Deploy established quantum control protocols from the open literature

Common questions about Boulder Opal