Quantum control infrastructure software for R&D professionals building the future

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Discover how Boulder Opal's control design and automation software enables you to build superior quantum computers and quantum sensors

An introduction to the capabilities and benefits of Boulder Opal

Get an overview of functionality and applications

Get an overview of Boulder Opal's functionality and applications

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

Use for quantum computing

Use Boulder Opal for quantum computing

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

Use for quantum sensing

Use 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

Discover workflows for research

Discover Boulder Opal workflows for research

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

Compare control-design (optimization) strategies

Compare control-design (optimization) strategies in Boulder Opal

Evaluating Boulder Opal performance across multiple benchmarks

Understand simulation and optimization benchmarks

Understand simulation and optimization benchmarks with Boulder Opal

Cite relevant Boulder Opal articles and specific documentation pages

Cite Boulder Opal

How to cite Boulder Opal

Adopt Boulder Opal

Find all of the resources you need to install and run Boulder Opal and immediately start solving your toughest problems

Authenticate your Boulder Opal session without a browser

Authenticate using an API key

How to authenticate using an API key

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

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

Understand computational resources in Boulder Opal

Mute status messages during calculations

How to mute status messages during calculations

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

Vizualize data using the Q-CTRL Visualizer

Set up Boulder Opal

Discover

Bring your unique quantum hardware to market faster with Boulder Opal's control design capabilities

Learn how Boulder Opal uses computational graphs to represent systems and perform operations

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

Get an introduction to graphs

Get an introduction to graphs in Boulder Opal

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

Learn to use graphs with piecewise-constant pulses

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

Represent quantum systems using graphs

How to represent quantum systems using graphs

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

Implement time-dependent functions

Implement time-dependent functions in Boulder Opal

Create parameterized signals for simulation and optimization

Leverage predefined signals in graphs

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

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

Tips and tricks to speed up your calculations in Boulder Opal

Improve calculation performance in graphs

Approaches to handle multidimensional data efficiently in graphs

Understand batches and broadcasting

Understand batches and broadcasting in Boulder Opal

Calculate with graphs

Model high-dimensional quantum systems and calculate time evolution leveraging time-varying noise and signal libraries

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

Explore different approaches to simulate quantum systems

Simulate and visualize quantum system dynamics in Boulder Opal

Learn simulation basics through the dynamics of a single qubit

Simulate the dynamics of closed quantum systems

Simulate closed, noiseless systems

How to simulate closed, noiseless systems

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

Simulate quantum dynamics subject to noise

How to simulate quantum dynamics subject to noise

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

Simulate quantum systems

Design control solutions for optimal or noise-robust performance on arbitrary quantum systems using model-based optimization methods

Generate and test robust controls in Boulder Opal

Learn to design robust single-qubit gates

Learn to design robust single-qubit gates using computational graphs

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

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

Incorporate smoothing of optimized waveforms

Add smoothing and band-limits to optimized controls

How to add smoothing and band-limits to optimized controls

Perform graph-based optimizations when gradients are costly

Optimize controls using gradient-free optimization

How to optimize controls using gradient-free optimization

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 controls from superpositions of basis functions

Optimize controls using arbitrary basis functions

How to optimize controls using arbitrary basis functions

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

Learn how to tune optimization hyperparameters in Boulder Opal

Tune optimization hyperparameters

Learn how to estimate noise resilience in Boulder Opal

Estimate noise resilience

Design model-based controls

Accurately characterize Hamiltonian parameters, identify noise sources, or probe unknown quantum systems

Build a system model using probe measurements and data fusion routines

Explore system identification techniques for characterization

Explore system identification techniques for quantum hardware characterization

Performing system identification with Boulder Opal

Learn to estimate parameters of a single-qubit Hamiltonian

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

Characterize hardware

Design

Boulder Opal's intelligent hardware and control automation

Connect Boulder Opal directly to quantum hardware in a closed loop to calibrate and optimize hardware without the need for a full system model

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

Automate quantum experiments with AI

Use closed-loop optimization without a complete system model

Learn to find optimal pulses with automated optimization

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

Create parameterized signals for closed-loop optimization

Understand the library of signals

Automate closed-loop optimization

Automate

Discover how Boulder Opal can help you solve the toughest challenges across hardware systems with complete code-based solutions

Learn how Boulder Opal can be applied to superconducting systems

Engineering and simulating gates in superconducting transmon-cavity systems

Learn to simulate with the superconducting system module

Increasing robustness against dephasing and control noise using Boulder Opal pulses

Design noise-robust single-qubit gates for IBM Qiskit

Increasing robustness against control noise using Boulder Opal pulses

Design noise-robust single-qubit gates for Rigetti Quil-T

Increasing robustness against crosstalk in a two-qubit entangling operation

Perform robust optimization for the cross-resonance gate

Perform model-based robust optimization for the cross-resonance gate

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

Demonstrate SU(3) gates on superconducting hardware

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

Design fast optimal SNAP gates in superconducting resonators

Engineering fast cavity state generation in superconducting cavity-qubit systems

Perform optimal Fock state generation

Perform optimal Fock state generation in superconducting resonators

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

Design error-detectable gates for dual rail resonators

Design error-detectable entangling gates for superconducting resonators in dual-rail encoding

Generating single flux quantum gates robust to leakage and frequency drift

Design error-robust digital SFQ controls

Design error-robust digital SFQ controls for superconducting qubits

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

Changelog