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Background information for the use of Boulder Opal in quantum research

An introduction to the capabilities and benefits of Boulder Opal

Overview

Boulder Opal overview

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

Quantum computing

Boulder Opal for quantum computing

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

Quantum sensing

Boulder Opal for quantum sensing

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

Workflows for research

Boulder Opal workflows for research

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

AI automation for quantum experiments

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

Computational resources in Boulder Opal

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

Visualizing your data using the Q-CTRL Visualizer

Simplify your workflows with convenience functions, nodes, and classes

Toolkits

Boulder Opal Toolkits

Create parameterized signals for simulation and optimization

Libraries of signals

Libraries of signals for Boulder Opal

Build a system model using probe measurements and data fusion routines

Characterizing your hardware using system identification

Characterizing your hardware using system identification in Boulder Opal

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

Choosing a control-design (optimization) strategy

Choosing a control-design (optimization) strategy in Boulder Opal

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

Choosing the approach to simulate a quantum system

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

Understanding graphs

Understanding graphs in Boulder Opal

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

Working with time-dependent functions

Working with time-dependent functions in Boulder Opal

Tips and tricks to speed up your calculations in Boulder Opal

Improving calculation performance in graphs

Approaches to handle multidimensional data efficiently in graphs

Batching and broadcasting

Batching and broadcasting in Boulder Opal

Topics

Introduction to the essential Boulder Opal features with hands-on exercises

Engineering and simulating gates in superconducting transmon-cavity systems

Simulate and optimize superconducting system dynamics

Simulate and optimize dynamics with the superconducting systems toolkit

Creating optimal operations with the trapped ions toolkit

Optimize Mølmer–Sørensen gates for trapped ions

Simulate and visualize quantum system dynamics in Boulder Opal

Simulate the dynamics of a single qubit using graphs

Simulate the dynamics of a single qubit using computational graphs

Generate and test robust controls in Boulder Opal

Design robust single-qubit gates using computational graphs

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

Get familiar with graphs

Use closed-loop optimization without a complete system model

Find optimal pulses with automated optimization

Performing system identification with Boulder Opal

Estimate parameters of a single-qubit Hamiltonian

Tutorials

Step-by-step how-to guides for the features in Boulder Opal

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

Monitor activity and retrieve results

How to monitor activity and retrieve results

Mute status messages during calculations

How to mute status messages during calculations

Cite relevant Boulder Opal articles and specific documentation pages

Cite Boulder Opal

How to cite Boulder Opal

Create graphs for computations with Boulder Opal

Calculate and optimize with graphs

How to calculate and optimize with graphs

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

Represent quantum systems using graphs

How to represent quantum systems using graphs

Use predefined signals from the Boulder Opal toolkit

Create analytical signals for simulation and optimization

How to create analytical signals for simulation and optimization

Use analytical expressions to construct your Hamiltonian

Define continuous analytical Hamiltonians

How to define continuous analytical Hamiltonians

Perform calculations using optimization results in a single graph

Perform optimization and simulation in the same calculation

How to perform optimization and simulation in the same calculation

Reapply graph nodes for multiple applications

Reuse graph definitions in different calculations

How to reuse graph definitions in different calculations

Perform graph-based optimizations when gradients are costly

Optimize controls using gradient-free optimization

How to optimize controls using gradient-free optimization

Highly-configurable non-linear optimization framework for quantum control

Optimize controls in arbitrary quantum systems using graphs

How to optimize controls in arbitrary quantum systems using graphs

Incorporate nonlinear Hamiltonian dependences on control signals

Optimize controls with nonlinear dependences

How to optimize controls with nonlinear dependences

Efficiently perform control optimization on sparse Hamiltonians

Optimize controls on large sparse Hamiltonians

How to optimize controls on large sparse Hamiltonians

Incorporate robustness into the design of optimal pulses

Create dephasing and amplitude robust single-qubit gates

How to create dephasing and amplitude robust single-qubit gates

Design pulses that minimize leakage to unwanted states

Create leakage-robust single-qubit gates

How to create leakage-robust single-qubit gates

Efficient state preparation using Mølmer–Sørensen-type interactions with in-built convenience functions

Optimize error-robust Mølmer–Sørensen gates for trapped ions

How to optimize error-robust Mølmer–Sørensen gates for trapped ions

Creating efficient gates for trapped ions without individually addressing the ions

Optimize Mølmer–Sørensen gates for a multitone global beam

How to optimize Mølmer–Sørensen gates for a multitone global beam

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

Optimize controls robust to strong noise sources

How to optimize controls robust to strong noise sources

Defining parameters of the optimization using the cost history and early halt conditions

Tune the parameters of an optimization

How to tune the parameters of an optimization

Configuring the stochastic optimizer by requesting the cost history from the optimization results

Tune the learning rate of a stochastic optimization

How to tune the learning rate of a stochastic optimization

Incorporate smoothing of optimized waveforms

Add smoothing and band-limits to optimized controls

How to add smoothing and band-limits to optimized controls

Incorporate time symmetry into optimized waveforms

Optimize controls with time symmetrization

How to optimize controls with time symmetrization

Optimizing over the duration of your controls

Find time-optimal controls

How to find time-optimal controls

Create optimized pulses using CRAB techniques

Optimize controls using a Fourier basis

How to optimize controls using a Fourier basis

Create optimized controls using Hann series basis functions

Optimize controls using a Hann series basis

How to optimize controls using a Hann series basis

Create optimized controls using arbitrary basis functions

Optimize controls using user-defined basis functions

How to optimize controls using user-defined basis functions

Reconstructing noise spectra using shaped control pulses

Perform noise spectroscopy on arbitrary noise channels

How to perform noise spectroscopy on arbitrary noise channels

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

Perform parameter estimation with a small amount of data

How to perform parameter estimation with a small amount of data

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

Perform parameter estimation with a large amount of data

How to perform parameter estimation with a large amount of data

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

Characterize a transmission line using a qubit as a probe

How to characterize a transmission line using a qubit as a probe

Calibrate RF control channels for maximum pulse performance

Automate calibration of control hardware

How to automate calibration of control hardware

Closed-loop optimization without complete system models

Automate closed-loop hardware optimization

How to automate closed-loop hardware optimization

Automate your calibration workflows with the Q-CTRL Experiment Scheduler

Automate complex calibration tasks

How to automate complex calibration tasks with Boulder Opal

Design waveforms using a model-independent reinforcement learning framework

Optimize controls starting from an incomplete system model

How to optimize controls starting from an incomplete system model

Simulate the dynamics of closed quantum systems

Simulate quantum dynamics for noiseless systems using graphs

How to simulate quantum dynamics for noiseless systems using graphs

Simulate the dynamics of closed quantum systems in the presence of Non-Markovian noise

Simulate quantum dynamics subject to noise with graphs

How to simulate quantum dynamics subject to noise with graphs

Evaluate the performance of multi-qubit circuits with and without noise

Simulate multi-qubit circuits in quantum computing

How to simulate multi-qubit circuits in quantum computing

Calculating the dynamics of a quantum system described by a GKS–Lindblad master equation

Simulate open system dynamics

How to simulate open system dynamics

Calculate the dynamics of a high-dimensional quantum system described by a GKS–Lindblad master equation

Simulate large open system dynamics

How to simulate large open system dynamics

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

Calculate the steady state of an open quantum system

How to calculate the steady state of an open quantum system

Calculate the Mølmer–Sørensen gate evolution characteristics for trapped ions

References

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Q-CTRL Visualizer

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