With our cloud-accelerated toolkit, there's no need to build or access local high-performance-computing infrastructure; we take care of it all for you. You can move faster right away with no fuss, no maintenance, and no issues with hardware maintenance.

Recent tests have shown that the Q-CTRL cloud architecture allows complex optimization tasks to run faster than any competitive package and over ~10x faster than on a standalone machine, reducing compute time from nearly an hour to just a few minutes.

How? By leveraging the nearly infinite cloud-computing capabilities readily used by the software industry, but hardly touched by quantum physics researchers.

Quantum control is growing in importance as quantum computing systems become more complex, new error sources appear, and customers demand ever increasing performance from hardware.

Numerical optimization provides amazing capabilities when it comes to developing error-robust controls used to implement the logic in a quantum computer. For instance, we've previously demonstrated that optimized controls run on IBM quantum computers can deliver up to 10X performance boosts to allow your algorithms to run with much higher likelihood of success.

And numeric optimization is the basis for a range of related tasks in quantum control, including system identification used to learn what’s happening inside hardware.

A default X-gate (left) versus Q-CTRL custom X-gates (right)
A default X-gate (left) versus Q-CTRL custom X-gates (right)

Q-CTRL provides a number of advanced numerical optimization tools in our Boulder Opal professional grade product. We favor robust control optimization - tolerant of small changes in the assumptions going into the numeric optimization, but also provide optimal control solutions which can efficiently be used to navigate a complex interacting system.

You access these techniques via the Python programming language, ensuring full integration with other numeric packages and even cloud-based hardware systems like IBM Q. The toolkit we have built, based on dataflow graphs, allows any user full flexibility to add constraints to their optimizations so they'll run successfully on real quantum hardware.

But there's another major advantage - speed.

Q-CTRL's dataflow graph architecture immediately builds compatibility with Tensorflow, dramatically boosting the performance of certain calculations, and seamlessly permits cloud-based performance acceleration.

What this means for our customers is enhanced performance in computationally complex tasks.

In a detailed technical manuscript, the Q-CTRL team demonstrated projected performance improvements up to 100X for complex optimizations performed on large systems. Calculations that could otherwise take days would complete in minutes.

Professor Michael Biercuk and NSW Transport Minister inspect a quantum computer at the University of Sydney. <br>Image by Brook Mitchell, Sydney Morning Herald.
Professor Michael Biercuk and NSW Transport Minister inspect a quantum computer at the University of Sydney.
Image by Brook Mitchell, Sydney Morning Herald.

And if you need more computational capacity, we can provide access to customized back-end resources tailored exactly to your needs.

It’s the perfect opportunity for the quantum technology research community to leverage the secret weapon of the software engineering sector - cloud computing!