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NASA’s Jet Propulsion Lab uses Microsoft’s Azure Quantum to ease Deep Space Network’s traffic jam

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NASA’s Jet Propulsion Laboratory monitors the Deep Space Network from Mission Control. (JPL Photo)

Microsoft has demonstrated how quantum-inspired algorithms can help smooth out Seattle’s snarled traffic, but can they solve NASA’s interplanetary data traffic jam?

Initial results from a project at NASA’s Jet Propulsion Laboratory suggests they can.

Microsoft’s Azure Quantum team says it’s been working with JPL to optimize the management of communications windows for the Deep Space Network. The network relies on giant radio antennas in California, Spain and Australia to handle communications with more than 30 space probes, including the James Webb Space Telescope and NASA’s Mars rovers.

Optimizing the schedule for communicating with all those probes requires intensive computer resources, especially because the DSN is having to deal with increasing demands for high-bandwidth data transmissions.

“Capacity is a big pressure,” JPL’s Michael Levesque, deputy director of the DSN, said in a recent news release.

Fortunately, schedule optimization is one of the sweet spots for Azure Quantum’s algorithms. Such algorithms are inspired by the principles of quantum computing — in which information doesn’t necessarily take the form of rigid ones and zeroes, but can instead reflect a range of values simultaneously during processing. The algorithms are run on classical computers rather than on quantum computers, which are still in their infancy.

In a blog posting, Azure Quantum reported progress in its effort to streamline JPL’s scheduling process. At the beginning of the project, the team recorded run times of two hours or more to produce a schedule. When quantum-inspired optimization algorithms were added to the mix, that time was reduced to 16 minutes. A custom solution handled the scheduling job in even less time — as little as two minutes.

The initial round of development produced a scheduling tool with a limited feature set, but Azure Quantum says it’s continuing to work on the project to incorporate a broader set of requirements.

JPL’s schedulers aren’t the only ones who stand to benefit from Azure Quantum’s optimization efforts. In 2019, Microsoft worked with Ford to reduce congestion in a simulation of the Seattle area’s traffic flow. In 2020, Toyota Tsusho took part in an experiment aimed at optimizing the timing of traffic signals. And last month, Microsoft and KPMG said they would test Azure Quantum’s capability to handle a wide range of real-world optimization problems.

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