Transport and logistics are replete with complex computational problems that can quickly become totally impractical - from identifying how to efficiently route cargo or passengers across multiple transit modes to updating city-wide transport schedules in response to changing conditions on the ground.
Even the most powerful machine learning algorithms can be quickly overwhelmed by the scale of the problems at hand. Quantum computing has already been deployed on a trial basis for managing traffic flow in Portugal, and optimizing aircraft scheduling. It may even prove useful for the last-mile logistics problem.
We build educational and developer tools that help transport and logistics experts take advantage of the quantum opportunity. And with our application partners we can help deliver custom solutions for your needs - from education to execution on real quantum computers.
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.
Improving algorithmic success for Transport for NSW's complex transport network management and congestion problems using quantum control infrastructure software
>200X
Improvement in algorithmic success.
A rare opportunity for leading transport innovators and quantum computing experts to tackle complex transport network management and congestion problems.