Drone flight risk management
with DIRAC-3
About the demo
As the use of drones continues to grow, so do the operational challenges associated with ensuring their safety and regulatory compliance. This demo illustrates the complexity in such problems, and shows how they can be solved with the Dirac-3 quantum optimization machine.
Minimize risk
Maximize efficiency
Improve scalability
Why time constraints matters
Creating a model that finds the least risky path for a given situation is largely unproblematic, but when a time contraint is introduced like we do in this demo, the problem becomes quite hard. In fact, it is a part of a class of problems known as NP-Complete, meaning that there is no known classical computing method that can solve these kinds of problems efficiently.
Other related use cases
Frequently asked questions
Demo setup
Results
Dirac-3's Route
Risk:
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Duration:
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Drone Survives:
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Your Route
Risk:
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Duration:
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Drone Survives:
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Improve scalability
×While this demo is relatively small scale, Dirac-3 is able to solve this NP-complete problem at polynomial time scale, even at scale up to its capacity. This is shown in our paper with the time-to-solution on a Dirac device vs. problem size of the LRTC model on one map. The fitting curve follows second, third, and fourth-order polynomials showing that our hardware does not explode in time-to-solution on an NP-complete problem
