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Q-CTRL teams up with Wolfram, Qblox, and others to deliver a complete quantum computing toolset for researchers and enterprises
February 27, 2024
Move faster with the most powerful control design and automation solution for the quantum era.
Compare plans<5min
Time to integration
>99.9%
2-qubit gate fidelities
<4 hours
Time to device bring-up
Quantum hardware teams must advance to bigger, higher performing devices, but these systems are notoriously sensitive and hard to control.
Boulder Opal provides cutting-edge control technologies through a familiar Python client so users can effectively design, automate, and scale quantum hardware.
With the Scale Up package, Q-CTRL uses industry-leading control expertise to collaborate on custom Boulder Opal solutions to solve critical performance and operational problems.
Quickly realize and expand your hardware's full potential
Push hardware to the limit with optimized control solutions
Accelerate workflows between theory and experimentation
Uncover critical system parameters that drive performance
Reach peak efficiency and performance with automation
Simplify repetitive characterization, optimization, and calibration tasks
Achieve performance beyond what's possible manually
Save hours through massive increases in efficiency
Reach commercial scale with bigger, higher quality devices.
Standardize control routines for consistency and speed
Maximize throughput while ensuring product quality
Abstract technical workflows so anyone can do them
Boulder Opal provides access to the key building blocks of critical quantum control techniques so users can uncover the true characteristics of underlying hardware and then optimize the performance of that hardware to its full potential.
This is all delivered through a local Python client and cloud-accelerated computational tools to provide the scalability and speed required for quantum hardware development and deployment.
Model quantum systems with integrated time-varying noise and signal libraries.
Design control solutions delivering peak performance based on the encoded quantum model.
Accurately define hardware specifications, hamiltonian parameters, and environmental noise.
Implement automated feedback loops for various design, calibration, and hardware-performance optimization tasks.
Orchestrate complex automated calibration and tuneup workflows constructed using Boulder Opal’s functions.
These powerful capabilities support hardware teams with impactful control techniques to address problems that are otherwise out of reach.
Boulder Opal integrates easily with common Python packages and a wide range of control electronics systems so you can add powerful new capabilities with minimal effort.
Qiskit, Quil, QuTip, ARTIQ
Quantum Machines, Qblox, Zurich Instruments, Tabor Quantum Solutions, Keysight
We support superconducting, trapped ion, neutral atom, and other types of quantum hardware
Start with our free Basic plan and upgrade if you need
to further accelerate research and development progress.
Basic
$0
Free Basic plan, yearly limits apply
For beginners, students, and explorers
Cloud software platform
4 vCPU, 32 GB RAM machine
1 machine (1 calculation at a time)
12 cloud machine hours
Standard support
Essentials
$1,500
USD / year
Managed compute for small teams
Cloud software platform
8 vCPU, 64 GB RAM machine
1 machine (1 calculation at a time)
200 cloud machine hours
Standard support
Performance
$9.99
$5,000
USD / year
Expanded computational resources for performance minded teams
Cloud software platform
16 vCPU, 128 GB RAM machine
Up to 4 machines (1 calculation per machine)
400 cloud machine hours
Standard + solutions engineering support
Professional
HPC-like resourcing for demanding teams of scientists and engineers
Hybrid cloud software platform
32 vCPU, 256 GB RAM machines
Up to 16 machines (1 calculation per machine)
1600 cloud machine hours
Dedicated + solutions engineering support
With Boulder Opal, Chalmers was able to design totally new numerically optimized gates that enable massive speedups without introducing new gate errors.
>180X
Reduction in gate duration.
It was really easy to go from code to experiments. I started from the relevant notebook in the documentation, followed the steps, adapted when necessary, and it simply worked! We’re now using Q-CTRL pulses that allow us to cut the time of our gates by eight times.
Q-CTRL's quantum control engineers developed a new machine-learning tool allowing high-fidelity reconstruction of the spectral “fingerprint” of quantum noise.
<1PPM
Sensitivity error-source identification during quantum logic.
Collaboration between experimentalists at University of Sydney and quantum control engineers at Q-CTRL breakthrough result published in Physical Review Letters
Nord Quantique used Boulder Opal to design a hardware-efficient QEC protocol for a superconducting system where quantum information is encoded in GKP states.
14%
increase in logical qubit lifetime
Given the complexity of the physics at play, being able to perform closed-loop optimization of a few physically motivated parameters of the quantum error correction protocol with Boulder Opal is very valuable to us.
With Boulder Opal, Northwestern suppressed 5 different noise sources simultaneously with a single optimized robust control pulse for atom interferometry.
5
different noise sources can be suppressed simultaneously with a single optimized robust control pulse for atom interferometry.
The breadth and flexibility of Boulder Opal allowed us to create our own optimization scenario and obtain pulses robust to the five most relevant experimental noise sources at the same time! This will be crucial in the development of atomic interferometers to detect dark matter and gravitational waves at currently unexplored frequencies.
Boulder Opal cloud licenses can accommodate an unlimited number of users. We provide recommendations for different license tiers for different group sizes to ensure that internal competition among users does not lead to computational delays.
Number of calculations can be run concurrently across multiple cloud-hosted machines to accelerate computation. Having a higher concurrency is useful when you want to speed up your calculations or share computational resources within your team.
Number of hours of running a single cloud-hosted CPU machine in a year. You can buy additional machine time to supplement your Essentials, Performance or Professional plan.
You can access a comprehensive documentation suite to help you on your journey, starting with Get started guide, and Tutorials.
A locally installed version of Boulder Opal can be provided upon request for circumstances mandating ultra-low latency in hardware communications. We recommend the cloud instance for general computations, as cloud services can provide performance far exceeding the specifications of the local machine.
We have independently validated and published technical validation of key demonstrations on hardware through our research - this includes device-level demonstrations of improvements >10X.We have also established a range of hardware validations with our customers and R&D partners around the world, collected in our case studies.