Train delays can cascade into stalled commutes, economic losses, and vacation snags. Scheduling trains is computationally complex, though: It can take hours or days to solve large transportation networks on traditional computers, when disruptions like train breakdowns or traffic accidents demand much quicker solutions. A new study led by researchers at the University of Maryland, Baltimore County (UMBC)—and focused on Baltimore's Light RailLink, a hybrid tram-rail network sharing roads with cars inside Baltimore City—harnesses quantum computing to address this challenge, using an approach that blends physics, computer science, and mathematics.
In their new paper, Sebastian Deffner , associate professor of physics; postdoctoral fellow Emery Doucet; doctoral candidate Reece Robertson; and collaborators Krzysztof Domino and Bartłomiej Gardas at the Institute of Theoretical and Applied Informatics in the Polish Academy of Sciences tested whether quantum devices could manage train schedules under real-world conditions. The team leveraged the "noise" inherent in quantum computers—random, unwanted disturbances that cause an effect similar to radio static—to model unpredictable train travel times.
Their results suggest that quantum computers can solve transportation scheduling problems, but more advanced hardware is needed to make using quantum devices practical, especially for larger networks.
Randomness on the rails
Doucet and Robertson discussed the work at Baltimore's Camden Yards Light Rail Station, while Orioles baseball fans unloaded from trains at regular intervals and delivery trucks rumbled by. Their enthusiasm cut through the urban din, their voices rising over the clatter and clank of passing Baltimore Light RailLink cars.
"How long it takes you to get between two stations where you have a lot of shared infrastructure in between—you can't really predict that precisely," Doucet says, gesturing toward the tracks and their intersection with a nearby stoplight. This randomness complicates scheduling, but the team's diverse expertise—spanning theoretical physics, algorithm design, and quantum hardware—facilitated creative solutions.
Noisy doesn't have to be bad
Current quantum computers are classified as "NISQ," or "noisy intermediate-scale quantum," pronounced "nisk." That means they're error-prone with only moderate power. Rather than fighting the noise, though, the researchers used it to mimic everyday randomness, like traffic delays.
"The 'N' in NISQ stands for 'noisy,' but that doesn't mean that the noise has to always be deleterious," Doucet explains. "We wondered if maybe we could use the noise that the device is subject to as a tool to model the chaos and randomness."
The team tested their approach on two different quantum computers, one made by IonQ , which is headquartered in Maryland, and the other by D-Wave . Each company's quantum devices use quantum bits, or qubits, slightly differently to process information. The research team was able to solve scheduling problems with up to 12 trains on D-Wave's system, which contains thousands of qubits, and only two trains on IonQ's 25-qubit system.
This proof-of-principle work demonstrates that quantum computers can tackle concrete problems, though they're not yet faster or cheaper than classical supercomputers for large networks—the experiments cost about $65,000.
"What we've shown is that with the currently available hardware, you can already solve practical problems," Deffner, senior author on the new paper, says. The study highlights the need for larger, less noisy quantum systems to handle bigger networks.
Merging expertise, expanding possibilities
The potential impact is significant; rapid rescheduling could prevent network-wide disruptions.
"If you have an issue on a train network, everything has to stop until you reschedule, at least in that region—and the longer it takes you to come up with a new schedule, the more disruptive the original problem becomes," Doucet noted, as a train coasted noisily into the platform.
Robertson, a Ph.D. candidate in computer science, added, "Within the next few generations of quantum technology, the problems we could address will get larger, approaching problems that are intractable on current hardware."
Robertson's computer science background complements the team's physics expertise.
"Someone else might be able to help me with physics intuition, and then I can help them by suggesting an algorithm we could use to test their idea, or by applying some computational intuition that we could use in designing our quantum solution."
"Quantum information science is truly interdisciplinary," adds Deffner, who is also affiliated with the UMBC computer science and electrical engineering department and has master's level math training.
Beyond trains
This interdisciplinary, quantum-based approach could eventually optimize logistics, financial portfolios, or drug discovery—fields with complex, random variables. The study, funded through Deffner's fellowship at the National Quantum Laboratory , involved coding, theoretical modeling, and experiments on real quantum devices—a departure for Deffner's typically theory-focused research group.
Working on the Baltimore system was a fun challenge, Deffner says, because the LightRail Link transitions from operating as a train unaffected by traffic outside the city to a tram navigating city streets and stopping at traffic lights inside Baltimore. "Because of its unusual characteristics, it was just a unique problem. And of course, it's cool to work on a local system," Deffner says.
By uniting diverse expertise, UMBC's team is turning quantum noise into a strength, paving the way for efficient solutions to real-world problems.
