Maggie Williams ’24, physics, is a second-year physics Ph.D. student, a Quantum Science Institute (QSI) Graduate Fellow, and an officer in the Physics Graduate Student Association (PGSA). In her research with Sebastian Deffner, associate professor of physics, she studies biological processes at a microscopic scale, where the physics of atomic interactions—including quantum effects—come into play. Outside the lab, Maggie enjoys playing guitar, listening to music, hiking, and reading. 

On November 6, Maggie is giving an interactive live demo of quantum key distribution, where participants can experience firsthand how quantum physics makes eavesdropping detectable and secure communication possible. All are welcome. Take it away, Maggie! 

Q: What brought you to UMBC for graduate school, and how did your path at UMBC begin?

A: I applied to graduate school because I wasn’t done learning. As a UMBC undergraduate, my first upper-level physics course was Thermal and Statistical Physics, taught by Dr. Deffner. It was very challenging, but it sparked my interest in statistical physics, and soon I began sitting in on Dr. Deffner’s research group meetings to learn more about the field and what research involves. Not long after, I completed a research project under his guidance and presented a poster at UMBC’s 2023 Undergraduate Research and Creative Achievement Day.

Working with Dr. Deffner led me to begin reading current papers and graduate texts to better understand my work, which only made me want to learn more. Since I already knew about the strong, close-knit community at UMBC from my undergraduate experience, choosing UMBC for graduate school felt like the best option for my success. 

Earning my bachelor’s degree was not without challenges, though. After struggling through classes during the COVID-19 pandemic, I felt burned out and unsure about continuing my education, so I took a gap semester in spring 2022 to complete an internship at the tourism organization Visit Baltimore. After some time in the corporate world, I decided to finish my bachelor’s degree and returned to UMBC that fall. During my time away, UMBC advisors and faculty checked in and helped map out a clear path to graduating, which made my transition feel more manageable.

Q: Tell us about your current research and what fascinates you about it.

A: I study nonequilibrium statistical physics, which uses mathematics to model systems heavily affected by randomness. Right now, I’m finishing a project on small, active processes inside cells that are not in balance, with a particular focus on how cells copy information from DNA to RNA, and how tiny errors can build up as an organism ages. Because cells are so small, their behavior is naturally random, and events in the cell that are far apart in time barely affect each other.

That makes Markovian dynamics, which assumes the next step of a system depends only on its current state, a good framework for modeling these systems. This project also provides a simple testbed for future studies that aim to model microscopic biological processes.

In my graduate work, the idea that mathematics, combined with physical intuition, can effectively model complex quantum systems continues to fascinate me. I plan to focus my Ph.D. research on quantum many-body systems, which are large groups of tiny particles (like electrons or atoms) that follow quantum rules (such as being able to act like waves or exist in multiple states at once) and whose behaviors are tightly interconnected, so you have to consider how they all influence each other at the quantum level.

Q: What do you love about UMBC’s physics program and the community that supports you?

A: One thing I love about my program is its small-but-mighty community, which makes it easy to reach professors or senior grad students for support, advice, or just a chat. My cohort has inspired me to push toward my goals and has been a steady support network when classes or work get overwhelming, so I’m genuinely grateful for my peers at UMBC.

Q: How is being part of QSI benefiting your Ph.D.?

A: QSI fosters a welcoming community across disciplines that has helped me learn about fields I might not otherwise have the chance to explore. It also provides practical support, from funding for research materials and supplementary textbooks to travel support for conferences and meetings, and it offers chances to lead academic seminars!

Q: How are you involved on campus, and what do you enjoy about it?

A: I serve on the PGSA council. It’s a great way to boost morale among first- and second-year students, encourage socializing outside of class and work, and help us get to know each other. I also enjoy the event-planning side—it’s an opportunity to get creative and come up with fun group activities. The other council members are supportive and kind, and it’s been a great experience so far.

Q: What advice would you give to a high schooler or undergraduate interested in physics and/or quantum science?

A: Don’t hesitate to reach out to teachers and mentors: Ask questions, introduce yourself, and start conversations. Networking matters in every field, and it’s especially important in academia. I’d also encourage them not to dwell on setbacks. Graduate school is challenging, and I’ve had my own moments of discouragement and impostor syndrome. When that happens, I try to turn it into a chance to deepen my understanding, which has only fueled my curiosity about a subject.

In the end, a genuine interest in learning and the drive to keep pursuing knowledge are the most important ingredients. I believe these things have brought me to where I am today, and I’m excited to see where my journey through graduate school takes me. This is only the beginning!

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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 UMBC researchers—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 LightRail Link 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.