Built by the world’s leading team of professional quantum control engineers, Boulder Opal provides everything you need to improve and automate the performance of hardware for quantum computing and quantum sensing.
Simulate quantum dynamics
Design error-robust controls
Automate hardware calibration and optimization
Boulder Opal is designed for speed. You can move your project forward faster and deliver major results on tight timelines with the world-leading AI automation tools.
Outsource painful hardware tuneup, calibration, and optimization tasks to automated agents that interact directly with your hardware.
You can achieve dramatic improvements in quantum computer and quantum sensor hardware using Boulder Opal.
Robust control can reduce hardware sensitivity to noise and errors, and lab-validated noise characterization routines can help you pinpoint and eliminate sources of performance degradation.
You can save time in your team by leveraging professionally engineered tools that are fully documented and maintained. Better yet, you can get running in just minutes.
Boulder Opal integrates with experimental hardware, software packages and custom electronics.Learn More
Navigational stability improvement with hybrid quantum navigation system.
This groundbreaking application of autonomous quantum sensors in space exploration will be invaluable in leveraging extraterrestrial resources to establish permanent human bases on the Moon, Mars and beyond.
Steven Marshall, Premier of South Australia
Gate-level hardware improvement with Q-CTRL robust gates.
We used Qiskit Pulse and Q-CTRL’s Boulder Opal to run error-robust quantum gates on a five-qubit IBM Quantum Canary processor. These results show just how powerful pulse-level control can be for programming a quantum computer over the cloud.
Improvement in algorithmic success with Q-CTRL solutions.
This was a rare opportunity for some of our leading transport innovators and quantum computing experts to come together to tackle complex transport network management and congestion problems.
Andrew Constance, Minister for Transport and Roads
Sensitivity error-source identification during quantum logic.
The team at Q-CTRL was able to rapidly develop a professionally engineered machine learning solution that allowed us to make sense from our data and gain real insights into how to improve our hardware.
Dr. Cornelius Hempel
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.
Marina Kudra, PhD student at Chalmers
Improvement in gate robustness to amplitude miscalibration
Q-CTRL’s work has the potential to significantly improve algorithmic performance and hardware stability in quantum processors.
Alex Hill, Rigetti