User guides

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

Basics

How to monitor activity and retrieve results: Monitor job status and retrieve results from previously run calculations in Boulder Opal
How to represent quantum systems using graphs: Represent quantum systems for optimization, simulation, and other tasks using graphs
How to calculate and optimize with graphs: Create graphs for computations with the Q-CTRL Python package
How to format and export control solutions for hardware implementation: Prepare optimized controls for hardware implementation
How to import and use pulses from the Q-CTRL Open Controls library: Use pulses from an open-source library in Q-CTRL calculations
How to use QuTiP operators in graphs: Incorporate QuTiP objects and programming syntax directly into graphs
How to integrate Boulder Opal with QUA from Quantum Machines: Integrate Q-CTRL pulses directly into Quantum Machines hardware using the Q-CTRL Python QUA package

How to optimize controls in arbitrary quantum systems using graphs: Highly-configurable non-linear optimization framework for quantum control
How to tune the parameters of an optimization: Defining parameters of the optimization using the cost history and early halt conditions
How to optimize controls with time symmetrization: Incorporate time symmetry into optimized waveforms
How to add smoothing and band-limits to optimized controls: Incorporate smoothing of optimized waveforms
How to optimize controls with nonlinear dependences: Incorporate nonlinear Hamiltonian dependences on control signals
How to perform model-based optimization using a Fourier basis: Create optimized pulses using CRAB techniques
How to perform model-based optimization with user-defined basis functions: Create optimized controls using arbitrary basis functions
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 tune the learning rate of a stochastic optimization: Configuring the stochastic optimizer by requesting the cost history from the optimization results

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 optimize error-robust Mølmer–Sørensen gates for trapped ions: Efficient state preparation using Mølmer–Sørensen-type interactions with in-built convenience functions
How to calculate phase and motion dynamics for arbitrarily modulated Mølmer–Sørensen gates: Calculate the Mølmer–Sørensen gate evolution characteristics for trapped ions

How to simulate quantum dynamics for noiseless systems using graphs: Simulate the dynamics of closed quantum systems
How to simulate quantum dynamics subject to noise with graphs: 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 evaluate control susceptibility to quasi-static noise: Characterize the robustness of a control pulse to quasi-static noise
How to calculate and use filter functions for arbitrary controls: Calculate the frequency-domain noise sensitivity of driven controls

How to automate calibration of control hardware: Calibrate RF control channels for maximum pulse performance
How to automate closed-loop hardware optimization: Closed-loop optimization without complete system models
How to manage automated closed-loop hardware optimization with M-LOOP: Use external data management package for simple closed-loop optimizations
How to optimize controls starting from an incomplete system model: Design waveforms using a model-independent reinforcement learning framework

How to perform noise spectroscopy on arbitrary noise channels: Reconstructing noise spectra using shaped control pulses
How to perform Hamiltonian parameter estimation using a small amount of measured data: Estimate Hamiltonian model parameters using measured data and the graph-based optimization engine
How to perform Hamiltonian parameter estimation using a large amount of measured data: Estimate Hamiltonian model parameters using measured data and the graph-based stochastic optimization engine
How to characterize the bandwidth of a transmission line using a qubit as a probe: Characterize transmission-line bandwidth via probe measurements and the graph-based optimization engine