Dan Hooper interviewed on “How to be Anything” podcast
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https://podcasts.apple.com/us/podcast/how-to-be-anything/id1823252850?i=1000721819442
News Archives
Physics-informed Neural Networks Enable High-Fidelity Quantum Gate Design Using Optimal Control
Researchers successfully employ physics-informed neural networks to design high-fidelity pulses for controlling quantum operations, demonstrating a flexible and robust method for optimising two-qubit gate performance.
Read the full article at: https://quantumzeitgeist.com/neural-fidelity-quantum-networks-physics-informed-enable-high-gate-optimal-control/Welcome, Prof. Joshua Foster!
Joshua Foster’s long-standing interest in computational tools is, he believes, what led him to research a range of theoretical physics, including dark matter, gravitational waves, and new physics. “What got me interested in studying theoretical physics in particular was the idea that you could study structures or ways of doing calculations that would enable you to make predictions or derive results that just wouldn’t have been possible with previous approaches,” he says.
Foster, who referred to himself “extremely Midwestern,” grew up in Indianapolis, attended Indiana University as an undergraduate, and the University of Michigan for his PhD. He joined MIT as a Pappalardo Fellow in the Center for Theoretical Physics, then Fermilab as a Schramm Fellow in Theoretical Astrophysics. In August 2025, he joined the UW–Madison physics faculty.
Please give an overview of your research.
I’m generally interested in problems that surround: 1) the optimal design of an astrophysical observation or a laboratory-based experiment, 2) serious phenomenological calculations that give us a good understanding of what a signal of new physics might look like, and 3) the application of statistics and data analysis to determine if new physics signals were hiding in data that was accessible to us all along.
My primary interest, at least historically, has been in dark matter. At present, all we can really say is that 85 percent of the matter of our universe is yet to be identified, so it seems like a rather urgent problem to understand what that is. It also seems to be one of the few unambiguous hints of new Physics. My research is generally focused on what often is referred to as indirect and direct detection. The idea behind indirect detection — meaning that dark matter or other signals of new physics might appear to us in astrophysical datasets — is that although it might be challenging directly observe dark matter or new physics phenomena, we might be able to observe its downstream effects in astrophysical contexts. For example, dark matter could be made up of particles that annihilate when they encounter one another, and doing so produces gamma-ray signals. Or, dark matter could convert to photons in extreme astrophysical environments, producing radio signals. I’ve been thinking a lot about how to perform optimal searches in radio data in the search of that data. Another possibility is, we say, okay, these systems are interesting but complicated and intrinsically messy. Then we might alternatively look for dark matter interactions with precision laboratory systems. That’s the two-pronged big picture: looking for new physics in astrophysical observables and looking for physics in laboratory-based searches.
Then lately I’ve been thinking quite a bit about gravitational waves, which I find exciting because they might let us probe the mysterious early universe. We typically look back in time by looking at photons that are coming to us from a very, very long time ago. There’s a certain time we can’t look past, which is when the universe was too opaque to photons, but gravitational waves should have freely propagated through the universe, providing us with a way of looking even further back in time. It might be our best chance at understanding the physics of the very highest scales that would have been active in the early universe.
What are the first one or two projects your new group will work on here?
A major focus of my research going forward will be on detection strategies for gravitational waves. One exciting possibility that I’ve been studying recently is that the roughly 60 years of lunar laser ranging data — high precision measurements of the Earth-Moon distance — could be used to detect gravitational wave backgrounds at frequencies that have been challenging to access by other technologies. In tandem, it’s nice to understand what the new physics theories are that can generate gravitational wave signals, either at the frequencies that we can access with lunar laser ranging or at the frequencies that are being accessed currently by, for example, pulsar timing arrays, but might also be accessed in the future by the upcoming LISA observatory. And so really understanding how to make optimal use of the data that these observatories are collecting and how to connect them with new ideas for how models of new physics can generate gravitational wave observations is something that I plan to focus on.
In conjunction, I am looking for radio signals of axions, which convert to photons in the strong magnetic fields which surround neutron stars. The facilities and technologies through which we can perform radio observations are constantly being improved and eventually are going to culminate in two upcoming observatories: DSA-2000 and the Square Kilometer Array. As we prepare for these upcoming facilities, there are both prototypes and pathfinder observatories that are collecting data right now. So I’m interested in using those existing datasets to, first off, perform searches that are already going to have reach unparalleled by any others, and to set the stage for future data collections and analysis efforts with these upgraded facilities.
What attracted you to Madison and the university?
Well, having begun this conversation by saying I’m very Midwest—I wanted to come back to the Midwest. And the department here has people with a broad set of expertise in many different technical fields that are all of interest to me. For example, in these contexts where I’m thinking about axion-photon interactions around neutron stars, the great challenge is understanding this complicated astrophysical environment. Here, there are experts in plasma physics, and there’s WIPAC, which is this incredible particle astrophysics center. The connections across campus in terms of the emerging data science focus also made me feel like this was a place where I would have colleagues with strong overlapping interests.
What is your favorite element and/or elementary particle?
I like helium. We can use helium-3 and helium-4 to make things very, very cold, and many of the experiments that I like to think about require extraordinarily cold systems to minimize thermal noise. They are only possible thanks to dilution refrigerators that pump helium in a manner that allows it to reach temperatures as low as 10 millikelvin. And Helium-3 has a number of other, to my mind at least, magic quantum properties. The number of interesting things that you can do with helium-3 seems to be limited only by your imagination.
My favorite particle is the axion. It’s my favorite dark matter candidate. And it might not exist in nature, but it is my favorite hypothetical particle. I hope it exists and that we find it.
What hobbies and interests do you have?
Cooking is my primary hobby. I like to eat—that’s part of it. But one of the joys of cooking is that you get to spend time on a craft. You can develop a skill and expertise, and you can measure your progress over time, and at the end of it, you eat the thing that you made, and then move forward with your life unburdened by your act of creation. So it’s also very low stakes. Other than cooking, I like to hike and I like to read.
2025 Nobel Prize Laureate John Martinis’s Connections to UW–Madison
UW–Madison builds on partnerships at Chicago Quantum Summit
The eighth-annual Chicago Quantum Summit was held Nov. 3-4, 2025 convening more than 500 top industry, government, and academic leaders from around the world for dialogue aimed at shaping the future of quantum technology. Held in downtown Chicago, at the center of the globally recognized Illinois-Wisconsin-Indiana quantum hub, the two-day event highlighted breakthrough research, commercialization […]
Read the full article at: https://research.wisc.edu/uncategorized/2025/11/06/uw-madison-builds-on-partnerships-at-chicago-quantum-summit/Kyle Cranmer awarded inaugural prize for AI in science
Mark Saffman awarded 2026 APS Ramsey Prize
Mark Saffman, the Johannes Rydberg Professor of Physics and director of the Wisconsin Quantum Institute, won the American Physical Society’s 2026 Norman F. Ramsey Prize in Atomic, Molecular, and Optical Physics, and in Precision Tests of Fundamental Laws and Symmetries.
The Ramsey prize recognizes outstanding accomplishments in the two fields of Norman Ramsey: atomic, molecular, and optical (AMO) physics; and precision tests of fundamental laws and symmetries. Saffman won “for seminal developments of quantum information processing with neutral atoms that allow the investigation of many-body problems that are intractable by classical computing.” He shares the prize with Antoine Browaeys at the Institut d’Optique in France.
Saffman joined the UW–Madison physics faculty in 1999 with ideas for his research program but struggled to acquire enough funding. Then, he started reading theory papers about the relatively new field of quantum computing and how to develop qubits, or quantum bits.
“This was in an era when people were proposing all these different ideas for qubits,” Saffman says. “I read this paper about using Rydberg gates to entangle atomic qubits and thought, ‘This looks interesting, let’s do that.’ That was the smartest decision I ever made in my career.”
An atom can be induced into a Rydberg state by a strong laser, when one of its outer shell electrons is excited into a very high energy state. The atom is effectively much larger than usual, and can lead to interesting quantum properties. Relatively inexperienced in experimental atomic physics, Saffman approached Thad Walker, a professor in the department and an expert on how to laser cool atoms, about collaborating. A decade later, they had their major success: a Rydberg blockade.
“The basic interaction is that you excite one atom to a Rydberg state and then you cannot excite a second one close by,” Saffman says. “That blockade interaction lies behind the ability to do a logic gate — a CNOT gate — and entangle two qubits.”
A year later, Saffman and Walker demonstrated the first CNOT gate for atomic qubits. These qubits, also called neutral atom qubits, quickly are now one of the leading platforms for achieving fault tolerant quantum computing.
Over the next decade Saffman started to realize that building a fully functional quantum computer was not just a scientific effort, it was a major engineering effort, one that was likely outside the scope of an academic research group.
“It became clear to me that to compete at the forefront, I needed more resources. I wanted to go faster,” Saffman says. “So, I ended up joining forces with ColdQuanta (now Infleqtion), an existing small cold atom sensing and components company .”
Saffman brought his quantum computing ideas to the company as Chief Scientist for Quantum Information at Colorado-based Infleqtion in 2018, and the company now has a satellite office in Madison.
The partnership with Infleqtion did, in fact, accelerate Saffman’s research. In 2022, his group, including long-time scientist and group member Trent Graham, co-authored a paper with engineers at Infleqtion where they demonstrated the first quantum algorithm to be run on an atomic quantum computer. It was a huge proof of principle and significant step forward in the field.
Quantum information research has emerged as a major topic within the AMO physics community. At UW–Madison, Saffman has been a key player in that shift. In 2019, he helped develop the Wisconsin Quantum Institute, an interdisciplinary effort of all quantum information science and engineering researchers on campus. That same year, he was named the institute’s director.
“UW–Madison was one of the first places to have multiple serious efforts in qubits: Thad and I pioneered neutral atoms, (physics professor) Mark Eriksson pioneered silicon spin qubits, (physics professor) Robert McDermott has superconducting qubits,” Saffman says. “Now, a huge fraction of new faculty coming out of academia and starting their own groups are working in quantum information-related science and engineering, including many of our new faculty. The state of quantum computing at UW–Madison is very strong.”
Q-NEXT quantum center renewed for five years
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https://www.anl.gov/article/argonneled-qnext-quantum-center-renewed-for-five-years
Ho Fung Tsoi PhD’24 wins CMS thesis award
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https://cms.cern/news/cms-phd-thesis-award-winners-2024
Welcome, Prof. Mariel Pettee!
Interdisciplinary physicist Mariel Pettee uses techniques grounded in machine learning to study a range of topics that span high energy physics and astrophysics, with an ultimate goal of developing a better understanding of the fundamental physical building blocks of our Universe.
Originally from Dallas, TX, Pettee was a physics and mathematics undergraduate at Harvard University, a master’s student in physics at the University of Cambridge, and a PhD student in physics at Yale University. While pursuing a postdoc at Lawrence Berkeley National Lab, she also joined the Flatiron Institute in New York City as a guest researcher. She then joined the UW–Madison physics faculty as part of the RISE-AI initiative in August 2025.
Please give an overview of your research.
My background is in high energy physics, and that training has fundamentally shaped the way I approach my work. But over the past several years, I have become more of what you might call a “data physicist” — someone with physics expertise who works at the intersection of physics and data science. In particular, I’m interested in how machine learning can help us do interdisciplinary physics research and make discoveries using massive experimental datasets that would otherwise be out of our reach.
On a broad scale, my research touches on high energy particle physics and astrophysics through the lens of machine learning. Some of my work applies recent machine learning techniques to domain-specific problems such as anomaly detection, object reconstruction, and unfolding. Another part of my work explores core questions in machine learning in areas such as self-supervised learning and likelihood-free inference in a physics-driven way. I’m also interested in developing large-scale foundation models for broader scientific use.
What are one or two main projects you’ll have your group focus on first?
The field of scientific foundation models has been rapidly taking shape over the last couple of years, but there are still a lot of open questions to explore. By researching what might make training foundation models on fundamental physics data distinct from training on more common industry-standard data, I think there is significant potential to understand our data more deeply.
I’m interested in simultaneously incorporating information from multiple heterogeneous layers of a detector, e.g. time series, images, and point clouds, as well as across detectors. Early projects in this direction will develop a variety of self-supervised learning strategies on multimodal HEP and astrophysics data to understand how models can simultaneously incorporate many different types of measurements of the same physics objects.
I’m also interested in studying stellar streams, which are remnants of ancient galaxies or globular clusters being absorbed into the Milky Way and serve as interesting tracers of local dark matter. The first step is to simply detect more of them using unsupervised or weakly supervised anomaly detection: trying to learn with no labels or with imperfect or missing labels. We can use machine learning models to automatically detect resonant anomalies in data, and stellar streams emerge as resonant anomalies in velocity space due to their constituents’ shared origin.
I’m optimistic that we will also eventually be able to use aggregate stream information to better map local dark matter substructure. Beyond their immediate physics use cases, streams can also serve as a nice testbed for understanding the limits of domain transfer for foundation models due to their resonant properties: perhaps particle physics data, with its 3D point cloud structure and “bump”-like anomalies, has more shared information with streams from the perspective of a foundation model than one might initially expect.
What attracted you to Madison and the university?
I felt a strong fit with Madison and the university from my first visit. I think that’s a combination of the general spirit of the department, how warm and open it felt, and how much I admired the researchers that I met when I was here. Also, the nature of the position that I was offered gave me the kind of flexibility that I dreamed of — to work and move between these spaces of high energy physics, astrophysics, and machine learning with a lot of freedom.
What is your favorite element and/or elementary particle?
Well, I have to pick a particle! I got into physics because of the Higgs boson. I started my physics career as an undergraduate at CERN on July 1st, 2012, and then the discovery of the Higgs boson was announced three days later. So I think I have the Higgs to thank for really getting me energized about this field. Waking up so early that morning, witnessing those presentations, seeing hundreds of people buzzing with excitement, scribbling on chalkboards, popping champagne corks — it made me feel like I was in the center of the universe.
What hobbies and interests do you have?
I love the performing arts of all kinds—contemporary dance, theater, music. I’m a dancer, choreographer, and occasional actor and director. I’m also an amateur birdwatcher.