Get started with Boulder Opal autocalibration

What is Boulder Opal?

Hierarchy of workflows

Understanding virtual devices

Creating your first virtual device

Running your first job

Reviewing job results and device history

Setting up with Qblox controllers

Setting up with QuantumMachines OPX controllers

Supported architecture

Discover

Create and manage virtual devices

Run an automated routine

Run a custom experiment

Run coherence measurements

Retrieve and display data

Manually update device parameters

Debug failed routines

Apply

Autocalibration

Get started with Boulder Opal Toolkit

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Boulder Opal fundamentals for quantum theorists

Boulder Opal fundamentals for quantum experimentalists

Set up and install Boulder Opal

Use Boulder Opal documentation

Start using Boulder Opal

Get an overview of functionality and applications

Use for quantum computing

Use for quantum sensing

Discover workflows for research

Compare control-design (optimization) strategies

Compare performance against competitive products

Cite Boulder Opal

Visualize data using the Q-CTRL Visualizer

Adopt Boulder Opal

Authenticate using an API key

Manage and monitor your calculations

Understand computational resources in Boulder Opal

Mute status messages during calculations

Set up Boulder Opal

Get an introduction to graphs

Improve calculation performance in graphs

Understand batches and broadcasting

Represent quantum systems using graphs

Perform optimization and simulation in the same calculation

Reuse graph definitions in different calculations

Calculate with graphs

Implement time-dependent functions

Leverage predefined signals in graphs

Learn to use graphs with piecewise-constant pulses

Create analytical signals for simulation and optimization

Define continuous analytical Hamiltonians

Represent time-varying signals

Explore different approaches to simulate quantum systems

Learn simulation basics through the dynamics of a single qubit

Simulate closed, noiseless systems

Simulate quantum dynamics subject to noise

Simulate multi-qubit circuits in quantum computing

Simulate open system dynamics

Simulate large open system dynamics

Calculate the steady state of an open quantum system

Simulate quantum systems

Optimize controls in arbitrary quantum systems using graphs

Optimize controls with nonlinear dependences

Optimize controls on large sparse Hamiltonians

Optimize controls robust to strong noise sources

Add smoothing and band-limits to optimized controls

Optimize controls using gradient-free optimization

Optimize controls with time symmetrization

Find time-optimal controls

Optimize controls using arbitrary basis functions

Tune optimization hyperparameters

Estimate noise resilience

Design model-based controls

Learn to design robust single-qubit gates

Create dephasing and amplitude robust single-qubit gates

Create leakage-robust single-qubit gates

Evaluate control susceptibility to quasi-static noise

Design error-robust quantum logic gates

Explore system identification techniques for characterization

Learn to estimate parameters of a single-qubit Hamiltonian

Perform noise spectroscopy on arbitrary noise channels

Perform parameter estimation with a small amount of data

Perform parameter estimation with a large amount of data

Characterize a transmission line using a qubit as a probe

Characterize hardware

Design

Automate quantum experiments with AI

Learn to find optimal pulses with automated optimization

Automate calibration of control hardware

Automate closed-loop hardware optimization

Understand the library of signals

Automate closed-loop optimization

Automate

Learn to simulate with the superconducting system module

Design noise-robust single-qubit gates for IBM Qiskit

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

Perform robust optimization for the cross-resonance gate

Demonstrate SU(3) gates on superconducting hardware

Design fast optimal SNAP gates in superconducting resonators

Perform optimal Fock state generation

Design error-detectable gates for dual rail resonators

Design error-robust digital SFQ controls

Perform noise spectroscopy in superconducting hardware

Superconducting systems

Learn to optimize Mølmer–Sørensen gates for trapped ions

Calculate system dynamics for arbitrary Mølmer–Sørensen gates

Optimize Mølmer–Sørensen gates for a multitone global beam

Optimize error-robust Mølmer–Sørensen gates for trapped ions

Design robust, configurable, parallel gates

Design robust gates with parametric trap drive amplification

Trapped-ion quantum computing

Generate highly-entangled states in large Rydberg-atom arrays

Design robust Rydberg blockade two-qubit gates in cold atoms

Improve Z2 state generation in neutral atoms

Rydberg-atom quantum computing

Mitigate crosstalk in dense arrays of semiconductor spin qubits

Design robust pulses for dense singlet-triplet qubit arrays

Spin-qubit quantum computing

Design robust pulses for widefield microscopy with NV centers

Perform narrow-band magnetic-field spectroscopy with NV centers

Boost signal-to-noise by 10X in cold-atom sensors

Quantum sensing

Easily integrate Boulder Opal with common electronic controllers

Format and export control solutions for hardware implementation

Use pulses from Open Controls

Use QuTiP operators in graphs

Integrate

Boulder Opal

Q-CTRL Visualizer

Q-CTRL Open Controls

References

FAQs

Toolkit

Control optimization capabilities to power the development and deployment of the next generation of quantum hardware

Automated calibration platform for quantum processors

Install Boulder Opal, configure your credentials, and create a client session ready for your first job

Get started with Boulder Opal Scale Up and learn key concepts

Manage workflows for autonomous quantum hardware characterization and calibration

Understand how experiments, routines, and solutions compose into a three-tier calibration workflow

The hierarchy of workflows in Boulder Opal

Examine the structure, components, and state-management workflow of the virtual device that mirrors your QPU

Understanding virtual devices in Boulder Opal

Create virtual devices on the Boulder Opal Scale Up platform, list available ones, and retrieve data from them

Creating your first virtual device with Boulder Opal

Execute quantum characterization and calibration tasks

Running your first job with Boulder Opal

Retrieve job history, inspect execution summaries, and verify device state after running experiments and routines

Reviewing job results and device history with Boulder Opal

Setting up Boulder Opal with Qblox controllers

Connect a Quantum Machines OPX+ system to Boulder Opal

Setting up Boulder Opal with QuantumMachines OPX controllers

Identify the supported QPU models and controller vendor models that are supported in the Boulder Opal platform

Supported architecture in Boulder Opal

Task-focused guides for common calibration workflows

Create, inspect, and snapshot virtual devices that serve as a digital representation of your quantum hardware

How to create and manage virtual devices with Boulder Opal

Execute autonomous Routines to automatically characterize or calibrate a component(s) on your quantum hardware

How to run an automated routine with Boulder Opal

Define experiment parameters, execute individual quantum characterization tasks, and retrieve results

How to run a custom experiment with Boulder Opal

Measure T1 energy relaxation, T2 Ramsey dephasing, and T2 echo coherence times on a single transmon

How to run coherence measurements with Boulder Opal

Fetch job results, navigate execution history, and inspect virtual device state using the Scale Up client

How to retrieve and display data with Boulder Opal

Override QPU component parameters and gate calibrations directly on an existing virtual device

How to manually update device parameters with Boulder Opal

Diagnose and resolve failed calibration by re-running experiments with adjusted parameters

How to debug a failed routine with Boulder Opal

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

Run your first calculation with Boulder Opal Toolkit

Get background information on Boulder Opal and learn how to get started today

Start using Boulder Opal to harness the power of quantum control in your own research, or integrate directly into hardware

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

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

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

Discover the critical methods to represent time-varying noise and control signals

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

Create optimized control signals on arbitrary quantum systems using model-based control

Learn to optimize quantum logic gates capable of suppressing common noise sources

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

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

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

Learn how Boulder Opal can be applied to trapped-ion quantum computing

Learn how Boulder Opal can be applied to Rydberg-atom quantum computing

Learn how Boulder Opal can be applied to spin-qubit quantum computing

Learn how Boulder Opal can be applied to quantum sensing

Connect Boulder Opal to leading hardware and software packages in the quantum ecosystem

Export Boulder Opal pulses to use in real hardware

Easily integrate Boulder Opal outputs with common electronic controllers

Prepare optimized controls for hardware implementation

How to format and export control solutions for hardware implementation

Use pulses from an open-source library in Boulder Opal calculations

How to import and use pulses from the Q-CTRL Open Controls library

Incorporate QuTiP objects and programming syntax directly into graphs

How to use QuTiP operators in graphs

Module, class, and method reference for Boulder Opal software packages

Boulder Opal

Explore documentation

Autocalibration

Toolkit

Additional resources

Need support?

Learning center

Research