Topics
Background information for the use of Boulder Opal in quantum research
Basics
Boulder Opal overview
An introduction to the capabilities and benefits of Boulder Opal
Boulder Opal for quantum computing
An introduction to the application of Boulder Opal for key tasks in quantum computing
Boulder Opal for quantum sensing
An introduction to the application of Boulder Opal for augmenting the performance of quantum sensors in real environments
Boulder Opal workflows for research
Understand how and when to integrate Boulder Opal into your research: for theorists or experimentalists, new hardware, or established systems
AI automation for quantum experiments
An overview of how Boulder Opal's AI tools can be used to automate the tune-up and optimization of quantum hardware systems
Visualizing your data using the Q-CTRL Visualizer
An introduction to the purpose and functionality of the Q-CTRL Visualizer
Boulder Opal Toolkits
Simplify your workflows with convenience functions, nodes, and classes
Libraries of signals for Boulder Opal
Create parameterized signals for simulation and optimization
Characterize hardware
Characterizing your hardware using system identification in Boulder Opal
Build a system model using probe measurements and data fusion routines
Design error-robust controls
Choosing a control-design (optimization) strategy in Boulder Opal
An overview of choices and tradeoffs in control design for your quantum system
Calculate with graphs
Understanding graphs in Boulder Opal
An overview of how Boulder Opal uses computational graphs to represent systems and perform operations
Working with time-dependent functions in Boulder Opal
An overview of how time-dependent functions are represented in Boulder Opal graphs
Improving calculation performance in graphs
Tips and tricks to speed up your calculations in Boulder Opal
Batching and broadcasting in Boulder Opal
Approaches to handle multidimensional data efficiently in graphs
