Building an Error-Creating Quantum Computer

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Category: Department News

Published: Tuesday, March 04 2025 01:40

Alaina Green is happy to face a challenge. Before becoming one of Joint Quantum Institute's newest Fellows, she cruised around the Atlantic in a 34-foot sailboat with only her husband, occasionally facing waves as tall as a two-story building. 

“It was a little bit scary at times,” says Green, who is also a physicist at the National Institute of Standards and Technology and a UMD Assistant Research Scientist.  “We'd have these waves come up, and because they were so tall, they would be above us. One time we saw a dolphin—just like, I was looking up at a dolphin.”

When she isn’t navigating the open sea, Green spends most of her time facing the challenges of lab-bound quantum computers—meticulously aligning lasers and wading through pages of mathematical calculations. In fact, the difficulties she found in physics were what originally drew her to the field.

“When I was getting ready to go to college, I was actually really on the fence about whether I was studying literature or physics, which is crazy,” Green says. “Ultimately, I chose to study physics because it was a little bit harder. I've always scored better on my reading and writing tests than my math. I really enjoyed both of them, but physics was more of a challenge.”Alaina Green with UMD graduate student Matthew Diaz working on an equipment in Green's lab. (Credit: Connor Goham)

That decision led to her studying physics and working in physics labs as an undergraduate at Lewis & Clark College in Portland, OR and as a graduate student at the University of Washington in Seattle. In those labs, she learned how to use lasers to manipulate atoms and molecules and study both their properties and environments. Then she joined JQI as a postdoctoral researcher and began to use lasers as part of a trapped ion quantum computer, which relies on lasers to manipulate ions—electrically charged atoms.

“Dr. Green is a perfect fit for JQI,” says former JQI Fellow Norbert Linke, who was recently announced as the next director of the University of Maryland’s National Quantum Laboratory and whose lab Green worked in as a postdoctoral researcher. “Her background studying molecule formation in ultracold atomic gases, combined with her recent achievements realizing a diverse array of innovative quantum computing and simulation experiments in trapped ions, makes her an ideal JQI Fellow.”

In Linke’s lab at JQI, Green worked on getting the lab’s quantum computer running as reliably as possible and creating new applications for it. But as she makes her research plans for her own lab, she believes that the work of building bigger and better quantum computers is quickly becoming more suited to industry labs than academic experiments.

“I can't just build another quantum computer, which I think is slightly better, because I know that there are multiple companies who are going to be attempting to do that in order to satisfy their customers,” Green says. “But what I can do is go back and ask some more fundamental questions.”

Green has decided to apply the power of quantum simulations to studying quantum errors and how to correct them. Error correction is currently a major research topic since it is crucial to making quantum computers reliable enough to be widely useful. But Green doesn’t want to primarily focus on improving a computer by eliminating and correcting errors, which is a common approach in quantum computing research. Instead, she wants to build a quantum computer that intentionally fosters errors in simulated environments.

“I'm going to be using my atomic physics expertise to create these sorts of simulated environments, which I can controllably interact with my fairly good but not perfect quantum computer,” Green says. “I want to create this meta-simulator where you can say, ‘Okay, I'm going to have a bunch of this error, and I'm going to see how does this error correction protocol work—does it work?’ I sometimes call it my very bad, no good, horrible quantum computer.”

Using her computer, Green plans to systematically study errors. There are many different types of errors that pop up in quantum computers, and researchers have different proposals for the best ways to deal with them. Creating errors under conditions she controls will help her understand them and explore which error correction approaches work best in practice.

Creating Errors on a Firm Foundation

At first glance, Green’s goal of creating errors might not seem like much of a challenge since quantum computers are notoriously fickle and prone to errors. The slightest shaking or fluctuation of temperature can disrupt their operation. The difficulty in Green’s new project lies in creating errors that are convenient to study. 

Intentionally letting a quantum computer heat up or shaking its lasers will induce errors, but those errors aren’t useful for the systematic study Green plans to undertake. Deliberately creating a specific type of quantum error on demand isn’t trivial but instead requires a similar level of care and expertise as keeping accidental errors in check. Creating useful errors with her quantum computer will be every bit as challenging as her past quantum simulation projects. 

In her new computer, she plans to repurpose elements from some of her postdoctoral work performing quantum simulations—using a quantum computer to mirror the behaviors of another physical system. In particular, there are two notable examples of techniques from Green’s work in Linke’s lab that she expects to play important roles in creating useful errors.

One is a project where she and her collaborators performed a simulation of paraparticles—a hypothetical type of particle postulated by physicists in the 1950s. Paraparticles aren’t going to feature in her computer, but the tools Green and her colleagues used to simulate them will.

To simulate paraparticles, the researchers had to look outside the well-known toolbox of ways to make the various pieces of a quantum computer interact. The standard building block of a quantum computer is a qubit that can exist in multiple states at once (a potential upgrade from regular computer bits that must always be in one of two distinct states). However, the various designs of quantum computers generally include elements that aren’t currently utilized, but those pieces can still be put to use if a researcher knows their computer well enough.

In their paraparticle simulation, Green and her colleagues utilized previously untapped elements of their trapped ion computer that behave as bosons. Bosons are a category of quantum particles characterized by their ability to crowd into the same quantum state. The willingness of bosons to share a state allows them to behave very differently from ordinary qubits, which are built to behave as fermions and thus can’t share the same quantum state.

“There was this new resource of the bosonic degree of freedom, just kind of sitting there,” Green says. “Everyone has access to it, and no one was using it. And so, I felt really proud to be one of the first people to produce an interesting simulation using both the qubit degree of freedom and the boson degree of freedom.”

Moving forward with her new quantum computer, she plans to once again take advantage of the bosons present in her experiment. By using bosons to model an environment that can interact with the normal qubits, she can perform error-creating simulations without dramatically increasing the size of her computer. In December 2024, Green and her colleagues posted a paper on the arXiv preprint server describing their use of the approach to efficiently simulate subatomic particles interacting and sharing energy in one dimension. This experiment paves the way to more complex simulations involving interactions with the environment, including those related to quantum errors. 

A second experiment that Green worked on in Linke’s lab will provide another crucial tool. In it, she and her colleagues developed a technique for managing the temperature of a quantum state in pursuit of quantum simulations of black holes.

Black holes, which are so big they warp space around them, are about as different from the tiny, delicate qubits of a quantum computer as you can get. Surprisingly, some theories describing black holes bear a notable resemblance to theories describing qubits, particularly when it comes to their temperature and the ways that they lose information. In both cases, information is intimately tied to energy and isn’t something that can be simply erased. Instead, it becomes harder to access over time as energy moves around.

“There are these very deep and intimate connections between black holes and quantum computers, which sounds crazy,” Green says. “But it’s actually true.” 

Based on the similarities, researchers have proposed quantum simulations to model black holes from the comfort of a lab (an appealing option compared with trying to glean information across astronomical distances). The various proposals require that states at two different points in time during the simulation come together and interact. The theoretical models also predict that in both cases the temperature will affect the rate at which information is lost, so the simulations must create quantum states at several distinct temperatures to explore the theory fully. 

A quantum state will naturally change based on the temperature of its surroundings, but the process is generally messy. Just exposing qubits to a specific temperature may get researchers a quantum state at that temperature, but it is unlikely to be a specific desired state, such as one carefully chosen to simulate a black hole. So, the team wanted to develop a reliable way to create a desired quantum state at a desired temperature on command. 

Green and her colleagues didn’t perform a full simulation of a black hole, but they did figure out a way to craft quantum states corresponding to specific temperatures. The key to creating states at a desired temperature was the addition of qubits, which are each dedicated just to controlling the temperature of a single quantum state. The additional qubits make the simulation a little harder to run but effectively gave the group individual thermostats to control the temperatures of certain states. They successfully demonstrated the technique by deploying it in experiments that brought states from two points in time together, as is needed for future black hole simulations. The temperature control allowed them to show that information became inaccessible more quickly at higher temperatures.

Since warming is a prominent source of potential quantum computing errors, Green’s ability to simulate temperature fluctuations that occur when and where she wants them will also be valuable in her new computer.

The techniques developed for these projects, as well as the other expertise developed during her postdoctoral research, will be crucial as Green develops her error-creating computer and applies the power of quantum computers to studying quantum computing itself.

It Takes a Village to Do Quantum Research

Green chose to continue her research and build her new computer at JQI because it is part of a robust research community focused on quantum research. And, when constructing a new experiment from scratch, a scientist sometimes needs a friendly loan from a neighbor.

“Sometimes a big stumbling point can just be like a simple piece of equipment that's kind of specific that you just don't have, and you can't afford to take the time to buy—you know, it might not arrive for like two months,” Green says. “But having this critical mass of other people who do similar physics to you can be really helpful because they might have that piece of equipment that you need, even just to borrow it.”

Sharing ideas is also crucial. Quantum computing draws on many areas, including quantum optics, atomic and molecular physics, condensed matter physics and quantum information science. And developing quantum technology generally requires pushing the boundaries of both theory and experiment, so close collaborations between theorists and experimentalists can be invaluable. Green says local collaborations with theorists at JQI make it easier for her to work out potential stumbling blocks in advance and do experiments that push more boundaries than she could on her own.

Collaborations with researchers outside of quantum research are also valuable to Green. As the quantum computing industry matures, quantum computers are becoming useful tools for not only physics research but also other fields like chemistry and mathematics. Part of Green’s work is taking the science of manipulating atoms with lasers and translating that into a mathematical language that can be easily used to tackle research problems in unrelated fields. In her quantum computing research, Green has collaborated with chemists on simulating molecular orbitals, mathematicians who study game theory and combinatorics, and physicists investigating quantum thermodynamics.

“Working with people is my favorite part of being a physicist,” Green says. “It's always just very satisfying, when not only do you understand something, but you know that the person next to you understands something, and you both understand it, because you both brought a piece of the puzzle together. And more importantly, you had to articulate exactly what you meant to each other so the other person would understand what you were thinking. I love that opportunity.”

Green says she is particularly grateful for the chance to collaborate with the brilliant students at UMD, and as she builds her “very bad, no good, horrible quantum computer” she hopes that the students she is introducing to physics will help correct any errors she makes.

“The top piece of advice I give any student, especially those who are joining my lab, is that I am not always right and neither is anyone else who you think is more institutionally important than you,” Green says. “Kind of the mantra in my lab is, ‘If you're not contradicting me, you're not doing it right.’”

Original story by Bailey Bedford: https://jqi.umd.edu/news/new-jqi-fellow-wants-build-error-creating-quantum-computer

Researchers Play a Microscopic Game of Darts with Melted Gold

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Category: Research News

Published: Friday, February 28 2025 01:00

Sometimes, what seems like a fantastical or improbable chain of events is just another day at the office for a physicist.

In a recent experiment by University of Maryland researchers at the Laboratory for Physical Sciences, a scene played out that would be right at home in a science fiction movie. A tiny speck glinted faintly as it hovered far above a barren, glassy plain. Suddenly, an intense green light shone toward the ground and enveloped the speck, now a growing dark spot like a meteorite or UFO descending in the emerald beam. Once the object crashed into the ground, the light abruptly disappeared, and the flat landscape was left with a new landmark and treasure for physicists to find: a chunk of gold rapidly cooling from a molten state.

This scene, which played out at a minuscule scale in repeated runs of the experiment, was part of a research project on nanoparticles—objects made of no more than a few thousand atoms. Each piece of gold was a bead hundreds of times smaller than the width of a human hair. In each run, the golden projectile was melted by a green laser and traveled almost a million times its own length to land on a glass slide.

Nanoparticles interest scientists and engineers because they often have exotic and adaptable properties. Unlike larger samples of a material, a nanoparticle can undergo dramatic changes with only small tweaks to its environment or size. For instance, a tiny gold nugget in a California stream has the same melting point, reflectivity and thermal conductivity as a 400-pound block of gold in Central Park, but two gold nanoparticles that differ in diameter by mere billionths of a meter have significantly different properties from the large pieces and, more importantly, each other.

The broad range of properties that nanoparticles have makes them a versatile toolbox for researchers and engineers to draw from. For example, people have used gold-based nanoparticles to detect the influenza virus, deliver medications in the body, and achieve a variety of vibrant colors in stained glass. However, since nanoparticles are so small and easily influenced, researchers must use a variety of specialized tools to study them.

When examining nanoparticles, some properties are best measured by tools—like a scanning electron microscope (SEM)—that get up close and personal with the sample. An SEM can get phenomenal detail on the size and shape of a nanoparticle if it is attached to a larger material that is easy to move and handle. However, the small size of nanoparticles can make other properties, like how they conduct heat, almost impossible to measure if they are touching anything. The mere presence of larger objects can often alter a nanoparticle’s properties or drown out its interaction with the measurement device. Fortunately, many nanoparticles can be isolated from the influence of other materials by using electric fields to levitate them, allowing researchers to use lasers to study certain properties, like heat conduction, from a distance.

JQI Fellow Bruce Kane and UMD researcher Joyce Coppock perform levitation experiments to study tiny pieces of graphene, which are sheets of carbon atoms. And in their quest to develop new tools, they have also turned their attention to tiny gold beads.

However, Kane and Coppock aren’t satisfied with the insights available from levitation experiments alone. They want the best of both worlds: to measure a sample levitated in isolation and then recover it for direct inspection. So, the pair are developing a method to recover tiny samples after they are released from the fields levitating them. In a paper published in Applied Physics Letters, the pair described how they were able to deposit gold nanoparticles on a slide after levitation and how they refined the technique to hone their aim. They hope mastering the process with gold will be useful in future experiments depositing more finicky graphene samples.

Before experimenting with depositing gold, Kane and Coppock had initially tried depositing graphene nanoparticles. Levitation is important for studying graphene on its own because its thickness—just a single atom—makes it challenging to study certain properties when it’s sitting on top of another material. For instance, a bulky material under a piece of graphene generally retains or moves heat around much more dramatically than the graphene, overwhelming any attempts to measure the heat conduction of the graphene itself. Additionally, simply sitting atop another material is often enough to stretch or squeeze a graphene sample in ways that change important properties, like its electrical resistance.

To avoid these issues, Kane and Coppock typically levitate their graphene samples in a vacuum. But the properties best measured directly without levitation are required to get a complete picture of a nanoparticle.

Ideally, Kane and Coppock would like to do both styles of measurement on individual nanoparticles. However, the existing levitation procedure makes it impractical either to perform direct probes on a sample before levitating it or to recover a sample once they remove the electric fields. That’s because there isn’t a convenient way to select a single tiny particle and reliably drop it into the field or recover it from the field.

In their experiments, Kane and Coppock first create an electric field designed to capture charged particles inside a vacuum chamber. To levitate a sample, they fill the chamber with many charged nanoparticles and watch to see if one of them falls into the field. After they make their measurements of that lucky particle, it gets released and becomes just another anonymous, invisible nanoparticle scattered about the vacuum chamber.

But Kane and Coppock had an idea for how to recover samples. Instead of just dropping the electric field and letting the particle fly in a random direction, they realized they could adjust the field to give it a shove in a particular direction as they released it. Then they just had to see if they could get the tiny projectile to land in an area they could easily search.

The pair placed a removable glass slide coated with a thin, conductive layer in the chamber as their target. Connecting a charge sensor to the conducting film allowed them to detect if an electrical charge landed on the slide. They also pointed a camera at the slide. The camera couldn’t watch the nanoparticles as they traveled, but each nanoparticle is just large enough that it will normally show up as a change of a single pixel in the camera image.

The pair’s calculations suggested that if a graphene sheet lands flat on the prepared slide it should stick. However, when they tried out the experiment, they kept measuring a spike in charge at the target—suggesting it hit—but almost never spotted where the sample landed. They suspected that most samples were bouncing off the slide or landing outside the area their camera covered.

So, they simplified the experiment by switching their projectile. Instead of using sheets of graphene that need to land perfectly flat, they tried spherical gold nanoparticles, which can be more uniformly produced and don’t have a preferential orientation for making contact. Kane and Coppock were already familiar with working with gold nanoparticles from previous experiments in which they levitated them and melted them with laser light.

Similar to the graphene sheets, the gold spheres were detected by the charge sensor but then couldn’t be found in the camera image. So, Kane and Coppock applied their melting technique to allow each particle to squish a little when it lands, greatly increasing the chance of sticking. All that was required to melt the gold was to turn up the power on the laser they already had installed for studying samples.

“Lo and behold, the minute we started doing that, we started seeing images on the camera,” says Coppock. “So basically, what was needed was to increase the adhesion by melting the particle.”

After that, they could reliably find the particles. However, repeated tries revealed that a sequence of deposited samples tended to spread far apart on the slide. Being able to place a sample in a consistent area would make the technique more useful and increase their chances of finding deposited graphene samples down the road.

“It's like the problem that people have going to the moon, right?” says Coppock. “You're a tiny person on Earth, and you have to get yourself a long distance to the moon. If you just launched yourself off the Earth, there's no way you would hit the moon. If we just launched the particle out of the trap, there's no way it would both hit the substrate and we would know where it was on the substrate. Finding a 200-nanometer particle on a one-inch sized substrate is like finding a needle in a haystack.”

So, they started working on the consistency with which they launched their tiny samples. The same electrical charge that allows Kane and Coppock to levitate the particles, also allows them to guide particles on the way to the slide. They surrounded the path they wanted the nanoparticles to follow with metal rings and then applied a voltage to the rings during the journey. The applied voltage creates an electric field that nudges a nanoparticle back onto a narrower path if it starts to stray. The way the electric fields bend charged particles back to a central focal point resembles a glass lens focusing light, so researchers call the setup an electrostatic lens.

By experimenting with the voltages that they used to launch the sample and guide it along its path, they were able to change where the particles tended to end up. They adjusted the voltages from a low setting where the samples spread over an area roughly 3,000 micrometers wide to a higher setting where all the particles clustered in an area about 120 micrometers across.

Plots of where gold particles from repeated runs of the experiment landed. The colors of the dots reflect the voltages applied to achieve electrostatic lenses of various strengths. The weakest lens (light blue dots) spread the samples across an area that is about 3,000 micrometers wide, and the strongest lens (red dots) focused all the particles into a cluster just 120 micrometers across. The lower right frame has increased magnification to show the distribution of particles within the cluster created by the strongest lens. (Credit: Laboratory for Physical Sciences)

If the initial scatter area were scaled up to the size of a dartboard, then their improved aim was like clustering their golden darts well within the outer bullseye. This is even more impressive since the scaled-up version of each gold bead is a dart only as wide as a human hair and is being thrown from the equivalent of about 35.5 meters away—about 15 times the normal distance between a dartboard and the throw line.

Moving forward, Kane and Coppock hope to further improve their ability to focus samples into a particular area and to use their refined aim in attempts to recover deposited graphene samples.

Original story by Bailey Bedford: https://jqi.umd.edu/news/researchers-play-microscopic-game-darts-melted-gold

Norbert M. Linke to Return to UMD

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Category: Department News

Published: Wednesday, February 26 2025 10:04

The National Quantum Laboratory at Maryland (QLab) welcomes a renowned expert in quantum physics, computing and networking to serve as its new director, effective September 1, 2025. Norbert Linke, Ph.D., brings a decade of experience running a quantum computer user facility and conducting research on the applications of trapped atomic ions.Norbert Linke

With this appointment, Linke will return to the University of Maryland’s Department of Physics, where he worked as a faculty member from 2019 to 2022, and he will hold the first IonQ Professorship, an endowed position designed to support faculty focused on quantum computing research and advancing quantum strategy in Maryland and beyond. The IonQ Professorship was established with a $1 million gift from quantum computing firm IonQ and fully matched by the Maryland Department of Commerce. The match was made through the Maryland E-Nnovation Initiative Fund (MEIF), a state program created to spur basic and applied research in scientific and technical fields at colleges and universities.

Linke, who is currently a professor of physics at Duke University, co-invented several of the original patents that enabled the launch of IonQ, born out of UMD research and headquartered in College Park. The QLab was established in 2021 through a partnership between IonQ and UMD as the nation's first user facility to provide the global scientific community with hands-on access to a commercial-grade quantum computer. Housed in the Division of Information Technology and located in the Capital of Quantum in College Park, the QLab is dedicated to advancing quantum research and education.

"I'm honored to lead the QLab in its mission to make quantum computing accessible and drive innovation. I'm excited to work with the talented team here to push the boundaries of what's possible with this technology," Linke said. “President Pines gave QLab a motto, which is ‘Quantum for All.’ Following this, my vision for QLab is to provide broad access to the latest quantum resources for researchers, commercial stakeholders, as well as students and educators.”

The QLab fosters a vibrant quantum community, through its QLab Fellows and Global User Programs, as well as the QLab Collaboration Space, a dedicated hub for innovation that opened in 2023. The QLab also supports groundbreaking research through seed grants and collaborations with companies in the Quantum Startup Foundry, resulting in numerous publications and software development.

“Linke’s expertise and leadership will be invaluable as we continue to push the boundaries of quantum computing and foster a collaborative environment for innovation,” said Jeffrey K. Hollingsworth, vice president of information technology and chief information officer at UMD.

Linke's appointment comes at a time of rapid growth and development in the field of quantum computing, especially in the state of Maryland, where Gov. Wes Moore recently announced a $1 billion Capital of Quantum Initiative anchored by UMD and built on a landmark public-private partnership, in which the QLab is poised to play a key role.

Original story: https://umdrightnow.umd.edu/university-of-maryland-names-new-director-of-national-quantum-laboratory

About the QLab:
The National Quantum Laboratory at Maryland (QLab) is a national user facility that provides the scientific community with access to a commercial-grade quantum computer. Established through a partnership between IonQ and the University of Maryland, the QLab is dedicated to advancing quantum research and education and is housed in the Division of Information Technology.

Powered by Physics

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Category: Department News

Published: Thursday, February 20 2025 01:40

Leonard Campanello (Ph.D. ’20, physics) spent the last three years on an ambitious mission—helping billions of Google Maps users find exactly what they’re looking for.

“I worked on the search function for Google Maps: you move the screen to a section of the map where you want to look for restaurants or hotels or things to do, add filters or attributes, like it has to be dog friendly or have a waterfront view,” Campanello explained. “And you want Google Maps to give you the best answer every time.”

As a Senior Data Scientist at Google, Campanello’s work brought science to the search process, applying the interdisciplinary physics training he received as a Ph.D. student in Professor Wolfgang Losert’s lab at the University of Maryland. Working on the Google Maps team, Campanello put his experience with models, algorithms, and analytics to work to better understand Maps users and optimize their search results.

“So, when you first issue a search, there's a list of places in a particular order. That order is carefully controlled,” Campanello explained. “We’ve proven that changing ranking algorithm has a material impact on the user's experience, and, at the end of the day, we need to know, did we have a net positive or a net negative effect on users? And we always strive to go in the net positive direction.”

As a scientist, Campanello has always been passionate about finding the stories hidden in data and building statistical models that capture the essence of the data, putting his physics skill set to work to answer a question or solve a problem.

“At the core of many problems in both physics and data science, I think we are trying to understand the data generating process so that we can better explain the fundamental physical phenomena driving what we see,” Campanello explained. “We observe that applying a force results in some change in a measurable quantity, whether the subject is a Google Maps user or a cell under the microscope. What's going on in the background that's fundamentally causing that change? How can we use this information to better understand our world? That’s what we want to find out.”

All in on physics

Campanello was a strong student who went all in on science and math since high school and earned a bachelor’s degree in physics from St. John’s University in 2013. Then, still unsure about how physics would translate into a future career, Campanello decided to pursue his Ph.D. at UMD, where he would have access to various options.

“I didn't know that what I wanted to do with enough certainty that I could commit to a graduate school that was kind of one dimensional,” Campanello recalled. “UMD had a massive physics department with a diversity of people in experiment and theory, whether it was condensed matter or high energy or biophysics or whatever, and that range of options was what ultimately kind of pulled me to UMD.”

After spending his first year working in condensed matter theory, a class with Physics Professor Michelle Girvan gave Campanello a whole new perspective.

“The class was nonlinear dynamics of extended systems and to this day it's probably the most influential class I ever took,” Campanello said. “Her problem-solving approach, including using graph theory and complex systems models, which I was never exposed to before, was eye-opening. We could actually create mathematical representations of all of these phenomena that we see in the world. And I was just wowed.”

At Girvan’s suggestion, Campanello joined Losert’s lab and began his Ph.D. research quantifying and modeling different dynamic processes, specifically complex interactions in biological systems.

“We already knew what some of the interactions were, so we knew that if we put this immune cell in the presence of some material, the immune cell would react in a specific way, which we could also measure under a microscope,” Campanello explained. “So given this set of biochemical information on the way these things behave short-term, medium-term and long-term, we said, how can we fit mathematical models to the microscope data and then use this to make inferences about this system as a whole?”

Opportunities, collaborations and simulations

Campanello took advantage of many opportunities at UMD, from teaching multiple MATLAB Boot Camps on image processing, computer vision and data analysis to coaching teams of data science students for the annual university-wide Data Challenge competition. Meanwhile, his continuing work in Losert’s lab exposed him to a world of possibilities.

“Wolfgang gave me and everyone in his lab the opportunity to work on so many different projects and collaborations with the National Institutes of Health and others, whether it was fundamental cell biology to projects on the interface of immunotherapies and autoimmune diseases to cancer, it's just crazy how much exposure we had,” Campanello noted. “He would help us identify opportunities to apply our analysis and modeling tools, give us guidance on the projects, and then let us to run with it. I really appreciated that.”

Campanello earned his Ph.D. in August 2020 and continued to do research at UMD for about six months before landing a job at Citibank in early 2021, applying his experience in modeling and analytics to consumer banking. 

Later that same year, he accepted a very different kind of opportunity at Google, working with the team that supports Google Maps to evaluate, advance and improve its ever-expanding search functions and, later, new capabilities, thanks to the addition of artificial intelligence.  

“The team is like 30 or so engineers, product managers, designers, user-experience researchers, and I was the one data scientist,” Campanello explained. “One of my primary responsibilities when I first joined was to create metrics or measurements that were absolute—meaning not open to interpretation—and I spent a lot of time doing research in that area to ensure that those measurements aligned with what we wanted for the user. What do we measure to know if we made the experience better?”

A new opportunity

In February 2025, after more than three years at Google, Campanello left to join Optiver, an Amsterdam-based global market maker that buys and sells securities to provide liquidity to markets. In this new position, he’ll again leverage his physics skill set, this time as a quantitative researcher.

“Part of my role will be to help improve the team's predictions in order to make better trading decisions. Can we make predictions right now about what will happen later today or later this hour or even just one minute from now?” Campanello explained. “If we can put numbers to these things and build models that accurately predict outcomes, then we can ultimately use those models to improve liquidity for all market participants.”

Fascinated by finance—and still inspired by the power of physics—Campanello looks forward to this next opportunity to grow.

“I've always had an interest in finance and what I'm looking forward to the most in this new role is the ability to really further my skill set,” Campanello said. “I want to get more exposure to what's happening at the bleeding edge of modeling and data science in quantitative finance. And I think this will be a good avenue for me to do that.”

Written by Leslie Miller