New possibilities in measurement capability and new data to change the future.
Quantum sensors exploit the extreme sensitivity of quantum devices. This emerging application of quantum technology puts this fragility to work by helping you to detect smaller signals from greater distances and unlock new capabilities that were never before possible.
The market is growing rapidly. Now is the time to get ahead of the competition.
Our focus on quantum control engineering is essential to extract more useful information from the next generation of quantum sensors and to accelerate their deployment in the field.
Quantum control allows you to overcome imperfections, environmental clutter, and platform noise in order to realize the true potential of your hardware.
Our infrastructure software solutions for quantum sensing expand your system’s performance where it counts - in the field. They are based on the power of Boulder Opal’s validated research tools, integrating autonomy, resilience, and noise rejection directly into your existing hardware and operational software.
Data is the heart of the modern economy. From underground to outer space, we measure everything around us to build the data streams we need to power the world.
We are creating new data streams for defense, minerals, long-term weather forecasting, and climate monitoring through our software-defined quantum sensing hardware. We go beyond hyperspectral imaging in order to provide continuous long-term mass change and magnetic signature monitoring.
We use quantum control to augment and fundamentally transform the performance of quantum sensors. This results in hardware that outperforms not only conventional designs but also allows user-defined reconfigurability.
Our expert team has built some of the highest-performing quantum sensors in the world to unlock new capabilities for our partners.
Discover how we are enabling the future of autonomous vehicles, powering a new generation of space-exploration missions, and providing a new set of eyes to see the earth with Q-CTRL’s “software-defined” quantum sensors.
Hardware and data from quantum sensors, powered by quantum control
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.