ExxonMobil’s leaders refer to this as society’s “dual challenge,” to provide reliable and affordable energy to a growing population, while also reducing environmental impacts and the risks of climate change.

“One near-term way to tackle that challenge is natural gas which emits up to 60% less greenhouse gases than coal. Making the switch from coal to natural gas for power generation will improve air quality and reduce carbon intensity,” says Dr. Vijay Swarup, ExxonMobil Vice President of Research and Development. “But the issue isn’t just one of production – it’s also a problem of transportation.”

Shipping LNG isn’t like shipping sneakers. LNG needs to be shipped ‘just in time’ as it is consumed, or people run out of power. Efficient shipping is absolutely critical.

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In 2021 more than 500 LNG ships are used to transport critical fuel supplies across the oceans. Together, they make thousands of journeys per year to destination ports where the LNG is deployed to power critical infrastructure.

Finding optimal routes for a fleet of such ships can be a mind-bendingly complex optimization problem. To efficiently transport LNG, each ship’s position must be accounted for on each day of the year, along with the LNG requirements of each delivery site.

Right now, this kind of problem can’t be solved exactly using classical computing. Even with a simplified problem involving just dozens of ships, the number of possible combinations of different decisions can reach 2^1,000,000. That’s greater than the total number of atoms in the universe.

Classical computers can tackle versions of this problem by breaking it down into digestible chunks and applying state-of-the-art mathematical methods. Even with this approach, it can take many hours to produce a useful solution, let alone an optimal one.

Scale up the problem to a larger fleet, or introduce uncertainties like weather or fluctuations in demand, and a problem this size rapidly becomes intractable - running up against the hard limits inherent to even the most advanced classical computing systems.

Quantum computers take a new approach to addressing this sort of complexity, with the potential to find solutions that classical supercomputer alone cannot handle. Industry leaders like Exxon are getting involved now to explore how blending classical and quantum computing techniques might solve big, complex, pressing global challenges.

Whole classes of optimization problems are close to impossible to solve exactly under the current paradigm, requiring businesses and institutions to accept a level of uncertainty and inefficiency in their processes. Though still in its infancy, quantum computing offers a revolutionary new approach that may help alleviate this challenge in the future.

Teams at IBM Research and ExxonMobil Corporate Strategy Research have collaborated to model maritime inventory routing on quantum devices, analyzing the strengths and trade-offs of different strategies for vehicle and inventory routing, and laying the foundation for constructing practical solutions for their operations.

As IBM’s quantum hardware scales rapidly – from small prototype systems to more promising larger devices – researchers are excited about the possibility to one day handle previously insoluble routing problems.

“By partnering with IBM Quantum, our aim is to ultimately level-up our ability to tackle more complex optimizations,” says ExxonMobil’s Swarup. “To solve bigger problems and make bigger differences.”