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

Boulder Opal

Get started

Get an overview of functionality and applications

Use for quantum computing

Use for quantum sensing

Discover workflows for research

Compare control-design (optimization) strategies

Understand simulation and optimization benchmarks

Cite Boulder Opal

Adopt Boulder Opal

Authenticate using an API key

Monitor activity and retrieve results

Understand computational resources in Boulder Opal

Mute status messages during calculations

Vizualize data using the Q-CTRL Visualizer

Set up Boulder Opal

Discover

Get an introduction to graphs

Learn to use graphs with piecewise-constant pulses

Represent quantum systems using graphs

Implement time-dependent functions

Leverage predefined signals in graphs

Perform optimization and simulation in the same calculation

Reuse graph definitions in different calculations

Create analytical signals for simulation and optimization

Define continuous analytical Hamiltonians

Improve calculation performance in graphs

Understand batches and broadcasting

Calculate with graphs

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

Learn to design robust single-qubit gates

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

Create dephasing and amplitude robust single-qubit gates

Create leakage-robust single-qubit gates

Tune the parameters of an optimization

Tune the learning rate of a stochastic optimization

Tune optimization hyperparameters

Evaluate control susceptibility to quasi-static noise

Calculate and use filter functions for arbitrary controls

Estimate noise resilience

Design model-based controls

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

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

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

Apply

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

Q-CTRL Visualizer

References

FAQs

Changelog

Automate

Automate closed-loop optimization

Automate quantum experiments with AI

Learn to find optimal pulses with automated optimization

How to automate calibration of control hardware

How to automate closed-loop hardware optimization

Understand the library of signals

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Additional resources

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Research