Gain your own quantum advantage. Tools to help you build your career in the quantum industry
Newcomers to quantum technology can go from zero background to programming real quantum computers with Black Opal - no PhD required!
Featuring interactive and accessible learning modules, students can gain practical skills in quantum computing in just minutes a day.
Black Opal is an educational tool that provides all the required math taught alongside quantum fundamentals with the learner in mind.
It replaces lectures and passive videos with engaging content developed by the world’s largest team of quantum control engineers.
Accelerate quantum research efforts using the world’s most
advanced quantum control infrastructure software
Research students building quantum computers and quantum sensors deploy our powerful AI-based agents to calibrate quantum hardware and quantum gates automatically, design optimized control solutions that perform better in the face of imperfect hardware, and even pinpoint critical hardware issues.
Researchers in quantum information, quantum sensing, and quantum control use our experimentally validated tools to explore and apply the most advanced concepts in quantum control in their work.
With a simple Python interface, our users can integrate industry-leading optimization engines and fully automated noise and error suppression tools right into their programming workflows, whether in numerics or cloud quantum computing hardware.
Application specialists and algorithm designers use our automated error suppression tools to gain better insights from their research efforts, faster. Now you can get the most out of cloud quantum computers without any specialized knowledge of error suppression technology, and zero configuration required.
Fire Opal is an out-of-the-box solution for minimizing error and boosting algorithmic success on quantum computers. It packages a comprehensive suite of best-in-class AI-driven quantum control techniques into a simple tool, letting you suppress errors in hardware and circuit execution with a single command.
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 Fire Opal, the Australian Army tested and validated a quantum computing solution on real hardware that promises to outperform their existing methods.
12X
improvement in the likelihood of finding an optimal solution with Fire Opal over the default hardware execution
Optimally routing 120 convoys can take more than a month of classical computation. The Australian Army is evaluating the potential of quantum computing to provide improvements; however, it’s been difficult to validate the feasibility of a quantum solution due to hardware noise. With Fire Opal, an algorithmic enhancement software, we are able to achieve results on quantum computers that build confidence in our quantum roadmap.
BlueQubit demonstrated groundbreaking loading of complex distribution information onto 20 qubits for a QML application by using our error suppression product.
8X
Better performance in terms of Total Variational Distance (TVD), which measures the deviation from perfect data loading.
As we develop novel techniques to solve some of the quantum industry’s hardest challenges, Fire Opal is an essential tool to reduce the impact of hardware noise and demonstrate successful results with deeper and wider circuits.
With Fire Opal a financial company was able to run algorithms on more cost-effective hardware systems while achieving results comparable to more premium systems
>2,500X
Reduction of quantum compute cost
We wanted to challenge Fire Opal’s capabilities by running a quite complex, unoptimized circuit. The results were extremely promising. The only comparable results we’ve seen have come from hardware that is currently too expensive to run extensive tests on.