Design
Bring your unique quantum hardware to market faster with Boulder Opal's control design capabilities
Calculate with graphs
Learn how Boulder Opal uses computational graphs to represent systems and perform operations
Get an introduction to graphs in Boulder Opal
An overview of how Boulder Opal uses computational graphs to represent systems and perform operations
Improve calculation performance in graphs
Tips and tricks to speed up your calculations in Boulder Opal
Understand batches and broadcasting in Boulder Opal
Approaches to handle multidimensional data efficiently in graphs
How to represent quantum systems using graphs
Represent quantum systems for optimization, simulation, and other tasks using graphs
How to perform optimization and simulation in the same calculation
Perform calculations using optimization results in a single graph
How to reuse graph definitions in different calculations
Reapply graph nodes for multiple applications
Represent time-varying signals
Discover the critical methods to represent time-varying noise and control signals
Implement time-dependent functions in Boulder Opal
An overview of how time-dependent functions are represented in Boulder Opal graphs
Leverage predefined signals in graphs
Create parameterized signals for simulation and optimization
Learn to use graphs with piecewise-constant pulses
Understand Boulder Opal graphs and nodes by smoothing a piecewise-constant pulse
How to create analytical signals for simulation and optimization
Use predefined signals from Boulder Opal
How to define continuous analytical Hamiltonians
Use analytical expressions to construct your Hamiltonian
Simulate quantum systems
Model high-dimensional quantum systems and calculate time evolution leveraging time-varying noise and signal libraries
Explore different approaches to simulate quantum systems
An overview of the choices and tradeoffs in deciding how to run a simulation
Learn simulation basics through the dynamics of a single qubit
Simulate and visualize quantum system dynamics in Boulder Opal
How to simulate closed, noiseless systems
Simulate the dynamics of closed quantum systems
How to simulate quantum dynamics subject to noise
Simulate the dynamics of closed quantum systems in the presence of Non-Markovian noise
How to simulate multi-qubit circuits in quantum computing
Evaluate the performance of multi-qubit circuits with and without noise
How to simulate open system dynamics
Calculating the dynamics of a quantum system described by a GKS–Lindblad master equation
How to simulate large open system dynamics
Calculate the dynamics of a high-dimensional quantum system described by a GKS–Lindblad master equation
How to calculate the steady state of an open quantum system
Compute the long time limit density matrix of Lindblad dynamics from a time-independent generator
Design model-based controls
Create optimized control signals on arbitrary quantum systems using model-based control
How to optimize controls in arbitrary quantum systems using graphs
Highly-configurable non-linear optimization framework for quantum control
How to optimize controls with nonlinear dependences
Incorporate nonlinear Hamiltonian dependences on control signals
How to optimize controls on large sparse Hamiltonians
Efficiently perform control optimization on sparse Hamiltonians
How to optimize controls robust to strong noise sources
Design controls that are robust against strong time-dependent noise sources with stochastic optimization
How to add smoothing and band-limits to optimized controls
Incorporate smoothing of optimized waveforms
How to optimize controls using gradient-free optimization
Perform graph-based optimizations when gradients are costly
How to optimize controls with time symmetrization
Incorporate time symmetry into optimized waveforms
How to find time-optimal controls
Optimizing over the duration of your controls
How to optimize controls using arbitrary basis functions
Create optimized controls from superpositions of basis functions
How to tune the parameters of an optimization
Defining parameters of the optimization using the cost history and early halt conditions
How to tune the learning rate of a stochastic optimization
Configuring the stochastic optimizer by requesting the cost history from the optimization results
How to calculate and use filter functions for arbitrary controls
Calculate the frequency-domain noise sensitivity of driven controls
Design error-robust quantum logic gates
Learn to optimize quantum logic gates capable of suppressing common noise sources
Learn to design robust single-qubit gates using computational graphs
Generate and test robust controls in Boulder Opal
How to create dephasing and amplitude robust single-qubit gates
Incorporate robustness into the design of optimal pulses
How to create leakage-robust single-qubit gates
Design pulses that minimize leakage to unwanted states
How to evaluate control susceptibility to quasi-static noise
Characterize the robustness of a control pulse to quasi-static noise
Characterize hardware
Accurately characterize Hamiltonian parameters, identify noise sources, or probe unknown quantum systems
Explore system identification techniques for quantum hardware characterization
Build a system model using probe measurements and data fusion routines
Learn to estimate parameters of a single-qubit Hamiltonian
Performing system identification with Boulder Opal
How to perform noise spectroscopy on arbitrary noise channels
Reconstructing noise spectra using shaped control pulses
How to perform parameter estimation with a small amount of data
Estimate Hamiltonian model parameters using measured data and the graph-based optimization engine
How to perform parameter estimation with a large amount of data
Estimate Hamiltonian model parameters using measured data and the graph-based stochastic optimization engine
How to characterize a transmission line using a qubit as a probe
Characterize transmission-line bandwidth via probe measurements and the graph-based optimization engine
