In the face of increasingly complex urban transit challenges, researchers from the University of Maryland, Baltimore County (UMBC) have pioneered an innovative quantum computing-based method to tackle the notoriously difficult problem of train scheduling. Their groundbreaking work, recently published in Scientific Reports, leverages the unique properties of existing noisy quantum computers to process unpredictable variables inherent in city transit systems. This approach not only demonstrates experimental proof-of-concept results but also paves the way for future, more efficient public transportation management worldwide.
Conventional train scheduling algorithms struggle under the pressure of real-time disturbances such as train breakdowns or shifting traffic conditions. These disruptions can quickly cascade, causing widespread delays and economic setbacks. Traditional supercomputers, though powerful, often require hours or even days to evaluate and optimize schedules for extensive networks. The UMBC team’s research focuses on Baltimore’s Light RailLink—an urban transit system where trains share roadways with cars, adding an extra layer of complexity—and explores how quantum computing might deliver faster, more resilient solutions.
At the core of this research is a team of interdisciplinary experts, including Associate Professor of Physics Sebastian Deffner, postdoctoral fellow Emery Doucet, and Ph.D. candidate Reece Robertson. Collaborating with international colleagues from the Polish Academy of Sciences, the researchers have harnessed the inherent noise of quantum computing hardware as a tool rather than a hindrance. They cleverly utilized the “noisy intermediate-scale quantum” (NISQ) devices’ intrinsic randomness to simulate the volatile nature of travel times on the rails—turning a traditional obstacle of quantum computing into an advantage.
Quantum computers operate using qubits, which, unlike classical bits, can exist in superpositions, enabling simultaneous computation of multiple outcomes. However, current NISQ devices are susceptible to errors caused by environmental disturbances, quantum decoherence, and hardware imperfections. Instead of attempting to suppress this noise, UMBC’s team embraced it, using these fluctuations to model real-world uncertainty in transit delays. This novel adaptation offers an advantage over classical computers, which often require complex probabilistic models to emulate such unpredictability.
The team conducted their experiments on two distinct quantum platforms: IonQ’s trapped-ion quantum computer, featuring 25 qubits, and D-Wave’s annealer, which boasts thousands of qubits suited for optimization problems. While IonQ’s system could effectively handle simpler scenarios involving up to two trains, the D-Wave quantum annealer managed to solve scheduling problems for up to 12 trains simultaneously. These experiments validated that even today’s noisy quantum hardware has practical utility, albeit limited by scale and costs—estimated around $65,000 per experiment.
Despite their encouraging results, the researchers acknowledge that current quantum technology is not yet scalable or cost-effective enough to replace classical supercomputers for large-scale scheduling. The study underlines the urgent need for advanced quantum processors with higher qubit counts and reduced noise levels. Such improvements would enable the handling of more extensive transit networks and foster rapid, adaptive rescheduling in response to real-time disruptions, ultimately enhancing efficiency and reducing commuter frustration.
Emery Doucet highlighted the real-world implications while standing at Baltimore’s Camden Yards Light Rail Station, amidst the everyday sounds of trains arriving and crowds moving. He emphasized the intrinsic randomness in how long it takes trains to travel between stations, complicated by shared infrastructure and external factors like traffic signals. This unpredictability makes manual or classical algorithmic scheduling suboptimal. “Noise” in quantum computation thus becomes a natural ally to model this complex reality accurately.
Reece Robertson, with a strong computer science background, noted the interdisciplinary synergy driving their work. By combining expertise in physics, algorithms, and quantum hardware, the team designed innovative computational methods finely tuned to the quirks and opportunities of quantum processors. Robertson envisions future generations of quantum technology vastly expanding the scope of solvable problems, likely surpassing the capabilities of even the most sophisticated classical systems.
The profound interdisciplinary nature of this research highlights the current frontier of quantum information science, integrating physics, mathematics, computer science, and engineering. Sebastian Deffner himself exemplifies this integration through his diverse academic background spanning quantum physics, electrical engineering, and advanced mathematics. This amalgamation of skills is crucial to translating quantum theoretical constructs into tangible, experimentally verified solutions.
Looking beyond transit, this quantum approach’s potential is far-reaching. The successful utilization of quantum noise modeling could revolutionize logistics optimization, financial portfolio management, and complex scientific simulations such as drug discovery—fields where randomness and uncertainty are pervasive. By transforming quantum noise from a liability into a functional modeling feature, researchers open doors to innovative problem-solving paradigms previously considered unattainable.
The UMBC team’s work was supported by the National Quantum Laboratory, led by Deffner’s fellowship. Their research involved the meticulous integration of theoretical modeling, software coding, and hands-on experimentation with cutting-edge quantum systems—a departure from Deffner’s mostly theory-oriented research trajectory. This hands-on approach was instrumental in demonstrating that quantum computing can provide real-world benefits even in its nascent stage.
Baltimore’s Light RailLink system presents a unique testbed, combining the characteristics of traditional trains with tram-like interactions in urban street environments that include traffic signals and shared roads. This duality introduces complex dependencies and variabilities in scheduling, making it an ideal challenge to showcase quantum computing’s potential to solve logistical puzzles where classical methods strain. It underscores that real-world transportation systems are filled with nuanced, stochastic variables requiring equally sophisticated computational approaches.
In sum, the UMBC-led research effort reveals a compelling narrative: quantum computers, even in their current noisy and limited form, can contribute meaningfully to solving complex urban transit scheduling problems. This proof-of-principle achievement is a significant milestone in the journey toward practical quantum applications. As quantum hardware continues to advance rapidly, such interdisciplinary initiatives position the scientific community on the cusp of a transportation revolution, where adaptive, real-time scheduling powered by quantum mechanics could redefine urban mobility for the 21st century and beyond.
Subject of Research: Not applicable
Article Title: On the Baltimore Light RailLink into the quantum future
News Publication Date: 12-Aug-2025
Web References:
- Original Paper on Scientific Reports
- UMBC Physics Faculty – Sebastian Deffner
- IonQ Quantum Computing
- D-Wave Quantum Annealing
- National Quantum Laboratory
References:
Deffner, S. et al. (2025). On the Baltimore Light RailLink into the quantum future. Scientific Reports. DOI: 10.1038/s41598-025-15545-0
Image Credits: Elijah Davis
Keywords: quantum computing, urban transit scheduling, noisy intermediate-scale quantum (NISQ), Light RailLink, quantum annealer, quantum noise modeling, optimization, Baltimore, interdisciplinary research
