Researchers Unlock High-Res View of 2D Materials by Doing a Microscopic Twist

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Published: Thursday, August 06 2026 01:34

By rapidly twisting a microscopically small tip back and forth, researchers at the University of Maryland (UMD) have unlocked a new way to detect subtle changes on the surface of a material flexing in response to infrared light.

In a paper published Aug. 5, 2026 in the journal Nature Communications, the researchers describe a new way to take high-resolution images that they call infrared torsional force microscopy, or TFM-IR for short. It allows them to measure the surface of a material with near-nanometer precision as it stretches and warps in response to infrared light—invisible light readily absorbed by many kinds of chemical bonds that causes them to vibrate. It’s the first technique that can measure both the vertical and horizontal vibrations induced by light with such high precision.Caption: A schematic of a novel infrared torsional force microscopy (TFM-IR) experiment conducted at the University of Maryland. A material sample (located on the dark gray disc) is illuminated with pulses from an infrared laser. A microscopic tip, twisted rapidly back and forth on a long arm, scans the surface of the material to sense its response to the light. A second laser is bounced off the arm to measure small changes to its twisting motion, allowing researchers to capture a detailed image of the sample's surface. (Schematic courtesy of the authors.)

“We have overcome a long-standing limitation in optical imaging,” says Min Ouyang, a professor of physics at UMD and a member of the Quantum Materials Center and the Maryland NanoCenter who led the new project. “Modern quantum materials derive many remarkable properties from variation that happens over distances of only a few nanometers, or even less. Conventional optical microscopes cannot really resolve this.”

The method Ouyang and his colleagues developed builds on atomic force microscopy (AFM), which was first developed in the 1980s and has matured into a standard technique for measuring the surfaces of material samples with extreme precision. Unlike traditional microscopes, AFM works more like your fingertips than your eyes. Instead of creating an image by collecting light with a lens, AFM drags a tiny tip across a material to feel the forces from its bumps and ridges. The technique can spot features that are smaller than a nanometer and, under the right conditions, can even resolve individual atoms.

By itself, AFM only sees the topography of a surface: It can sense that it’s higher over here and lower over there, but it doesn’t provide any details about the chemical composition of a sample. To learn about the makeup of a material, researchers often excite vibrations in a sample with infrared light and use the AFM tip to sense how the surface changes. Because chemical bonds respond in predictable ways to infrared light, the combination of AFM and infrared light can identify the signatures of particular molecules, either to verify the composition of a sample or to detect the presence of unwanted contaminants.

“If you shine some light on your sample, you're going to get some very slight thermal expansion,” says Yonatan Gazit, a graduate student in physics at UMD who is also the lead author of the new paper. “AFM is essentially measuring that thermal expansion to understand how well your sample is absorbing or interacting with the light.”

Modern AFM devices feature a sharp tip suspended above a sample on the underside of a skinny arm. Electronics rapidly drive the arm up and down, which gently taps the tip against the sample at a regular rhythm. The tapping helps the tip avoid getting stuck on a ridge, which would potentially damage material being studied, and enhances the sensitivity to infrared vibrations by closely matching their frequency. As the tip traverses the surface, the rhythmic tapping is altered as the material flexes and pushes against it. Researchers measure these subtle changes in tapping frequency by bouncing laser light off the top of the vibrating arm to monitor its motion.

The tapping technique is excellent for making sensitive measurements of height, but it doesn’t do a good job sensing how a material expands or contracts horizontally. In a paper published in 2024 in the Proceedings of the National Academy of Sciences, a team from Stanford University and their colleagues showed that twisting the AFM tip at a regular frequency instead of tapping it could measure previously undetectable variations along the surface of a double-decker stack of graphene, formed from two stacked layers of carbon atoms each arranged in a honeycomb pattern of repeating hexagons. They named their technique torsional force microscopy (TFM).

Caption: Two torsional force microscopy (TFM) images of bilayer graphene. On the left, standard TFM captures the material's signature honeycomb lattice. On the right, the new method (TFM-IR) captures much more detail about how the chemistry of the surface responds to infrared light. (Images courtesy of the authors.)Inspired by this result, Gazit, Ouyang and their colleagues designed an experiment that combined infrared illumination with the twisting technique. As the tip twists back and forth, pulses from an infrared laser periodically wash over a small sample of a material. Some energy from each pulse gets absorbed by the material, causing it to swell and vibrate. Choosing the frequency of the pulses—that is, how many pulses arrive at the sample per second— enables the tip to pick out either the vertical changes or the horizontal changes, similar to how a strobe light can selectively pick out or freeze certain kinds of motion.

As a proof of concept, the researchers tested the new technique by studying the surface of a small piece of mica, a shiny and flaky mineral used in manufacturing everything from drywall to tires to fireproof material for industrial ovens. They chose mica both because it’s already well-understood and because the chemical bonds that hold it together point along different directions, making it a good candidate for measuring both the vertical and horizontal vibrations induced by infrared light.

The team showed that they could detect four vibration patterns in mica and demonstrated that they could distinguish the horizontal and vertical movement by using different infrared laser pulse rates. They zeroed in on a small bump—a mica nanobubble on the surface just a few nanometers tall—and carefully dragged the tip from the center of the bump to its edge. They compared the results of their measurements with simulations of the horizontal and vertical vibrations expected from the way the bubble strained and bulged, and they found that the locations of the strongest horizontal and vertical responses to infrared light lined up between theory and experiment.

The team next turned their attention toward a double layer of graphene, the same material studied in the paper that first introduced the torsional technique. On its own, graphene has intrigued scientists for more than two decades because of its unique electrical and mechanical properties. When it’s stacked into two layers, with one layer rotated by a small amount, it gets even more interesting. The two layers form what’s called a moiré material, and in 2018, researchers found that a very particular angle turned a moiré stacking of graphene into a perfect electrical conductor—a quantum effect that made the material a superconductor.

Researchers remained in the dark about the microscopic origins of the effect. Because a moiré material is only a couple of atoms thick, the tiny changes in the lattice that give rise to its remarkable properties cannot be revealed by simply scanning its height. The torsional trick introduced in 2024 pointed toward a new way to image these atomically thin materials.

In the new paper, the team examined a sample comprising two layers of graphene stacked together at a small angle. They compared a standard TFM image of the sample with an image taken using their TFM-IR approach—both taken of the same exact sample at the exact same spot. The standard TFM image clearly showed the material’s signature lattice of hexagons, but the TFM-IR image revealed a wealth of additional details. Instead of merely showing the shape of the lattice, TFM-IR showed for the first time how different chemical bonds in a single hexagon—including bonds within a single sheet of graphene and bonds between the two sheets—react to infrared light, revealing a unique vibrational fingerprint of the underlying material. Understanding this fingerprint and the way that it changes when the stacking angle changes could prove crucial to gaining a better understanding of moiré materials and their properties.

“Our technique combines three capabilities that are rarely available in a single measurement,” Ouyang says. “First, it brings optical imaging and spectroscopy to the nanoscale, providing spatial resolution down to nearly one nanometer. Second, it can distinguish directional responses within a material, allowing us to uncover anisotropic properties that conventional techniques cannot resolve, Third, it provides each material’s unique spectroscopic fingerprint. In other words, our technique doesn’t just show what a material looks like; it also identifies what it is and reveals the hidden physical processes that govern its behavior by mapping how it responds to light with nanometer-scale precision.”

Ouyang and the team hope that the technique will be a key tool in characterizing and even designing materials going forward, and they emphasize that it has the added benefit of working at room temperature. In particular, TFM-IR might be useful for semiconductor companies, who are on the hunt for techniques to spot defects in their chips. The authors say that a technique capable of mapping the mechanical signatures of local chemistry could guide the development of new advanced manufacturing processes and might even help researchers optimize next-generation nanoscale devices, including quantum sensors and photonic quantum computers.

Story by Chris Cesare

In addition to Ouyang and Gazit, the paper had three other authors: Son T. Le, an associate research scientist at the Laboratory for Physical Sciences (LPS) and in the Department of Electrical and Computer Engineering at UMD; Aubrey T. Hanbicki, a research physicist at LPS; and Adam L. Friedman, a physicist and technical director at LPS.

Researchers Explore How Quantum Computers—and Their Errors—May Enhance AI

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Published: Friday, July 24 2026 01:34

Quantum computing and AI are among the most rapidly developing modern technologies. AI, in the form of machine learning, has been deployed for decades to recommend movies and TV shows and make it easier to search for images. Over the past several years, large language models have permeated even more facets of daily life, from writing emails to producing images, videos and songs following requests expressed in a few written lines.

Quantum computers, on the other hand, have remained almost exclusively in labs at universities and a handful of companies. Nevertheless, many researchers and engineers developing them are already looking for the earliest applications and predict a bright future in which quantum computers excel at certain tasks, like drug development and enabling new cryptographic techniques.

Despite machine learning and quantum computing both being heralded as revolutionary technologies, neither is a magic solution to every problem. They are each the products of a long line of research advances and are both still under active study.An artistic representation of a quantum neural network identifying a handwritten digit. Each ball containing an arrow represents a qubit that is serving as a neuron in the network. The neurons are organized in layers, and connections between neurons in adjacent layers control how the information is processed as it moves through the network. The final layer identifies the most likely digit. (Credit: Chris Cesare/JQI)

As the two technologies continue to mature, researchers are beginning to investigate ways to utilize them together. A collaboration between JQI Fellows Alaina Green, Norbert Linke, Victor Galitski and their colleagues recently reported on new experiments that explore how quantum computing influences—and might improve—machine learning. They used a variety of quantum computers to run a simple neural network­—a type of machine learning inspired by the structures in real brains.

Neural networks are the foundation of many prominent AI technologies, including large language models like ChatGPT and Claude. They have been around for decades and have been used for many applications, including image recognition. For instance, banks used the neural network LeNet-5, which was developed in 1998, to identify handwritten zip codes. But making neural networks run on quantum computers is unexplored territory.

In an article published as an Editors’ Suggestion in the journal Physical Review Letters on July 22, 2026, the JQI researchers and their colleagues describe experiments running neural networks on several different quantum computing platforms. Their results demonstrated that a certain amount of intentional quantum randomness, arising from measurements, can be beneficial to a neural network. They also saw hints that the errors that plague current quantum computers might be able to play a useful role when running a neural network on a quantum computer.

“Measurement outputs on quantum systems are inherently random, and this can improve the performance of a neural net,” says Linke, who is also the IonQ Endowed Associate Professor of Physics at UMD, a senior investigator at the National Science Foundation Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and the director of the National Quantum Laboratory (QLab) at UMD. “Additionally, operations on current quantum hardware are often imprecise. This limited control produces more randomness that, if it's not too large, can further boost the neural net performance.”

Designing a Quantum Neural Network

The new neural network experiment implemented an approach that Galitski, who is also a Chesapeake Chair Professor of Theoretical Physics in the Department of Physics at UMD, and two graduate students working in his group proposed in 2025. Their goal wasn’t to use a quantum computer to outperform existing neural networks. Instead, they wanted to use quantum computers to investigate when incorporating quantum features is helpful or harmful to a neural network.

Neural networks operate by transforming an input through a sequence of calculations to accomplish a goal. The information contained in the input—usually represented as a list of numbers—flows through a connected network of artificial “neurons” that systematically breaks apart the information and recombines it using calculations associated with each of the connections. The particular calculations that the network performs are tailored by a process called training, which involves testing many example inputs and adjusting the connections between neurons until the network reliably produces a desired result.

The network developed by Galitski and his colleagues was trained using traditional computers to perform a task that is straightforward for a neural network: identifying handwritten numbers. They focused on recognizing digits because it is an established exercise used to test image processing and machine learning models. The well-worn task allowed them to train and test the neural network using a set of images called the Modified National Institute of Standards and Technology database (MNIST), which provides examples of handwritten digits in a convenient standard format.

Examples of digits included in the MNIST dataset. (Credit: Suvanjanprasai, CC BY-SA 4.0, via Wikimedia Commons)

Their neural network takes an image as an input and at the end predicts the number it most likely depicts. The ready-made database and well-understood task provided a convenient starting point for the group to explore how quantum ingredients influence a neural network.

Galitski and the graduate students in his group described a way to make qubits—the basic building blocks of quantum computers—function as neurons. Their method lets them use quantum measurements to control the amount of quantum randomness intentionally included in the neural network’s operation on each run. The group designed their neural network architecture to be compatible with any of the various types of quantum computers being developed. There are many ways to make a quantum computer by starting with different qubits. However, all quantum computers rely on shared quantum principles.

The team took advantage of the way all qubits store information in their quantum states to incorporate different amounts of quantum randomness into their proposed model. A qubit will always be observed in one of two states when measured, but in between measurements it can be in a mixture of the two—called a superposition—where it is only possible to know the probability of finding it in one state or the other. A qubit can be put in a superposition where one or the other outcome is more likely to occur, but it’s not definitively in one state or the other until a measurement destroys the superposition and one of the states is observed.

In the proposal, the final state of the neurons is one of the two unmixed states of the qubits, and superposition and quantum measurements are used to inject randomness into the neural network. The approach allows different experimental runs of the neural network to use different superpositions. Each superposition provides a different chance that, at each step, the results might randomly flip between states and potentially alter the identification the neural network makes. Or researchers can perform a test without randomness by leaving the qubits exclusively in one state instead of a superposition. The approach provides a way to compare the success of neural networks featuring different amounts of quantum influence as well as to judge how the neural network operated on different quantum computers.

Testing a Quantum Neural Network

To test the proposal with experiments, Galitski shared it with experts at IBM as well as Linke and Green, who is also a physicist at the National Institute of Standards and Technology and a senior investigator at RQS. Galitski requested their help testing the neural network on real quantum computers, and they all agreed the idea was worth pursuing.

IBM’s quantum computer is built using superconducting circuits, while Green and Linke have built their quantum computers using trapped ions—electrically charged atoms contained and manipulated with light waves. Linke and Green can operate their quantum computers by manipulating ions in two different ways—one using microwave light and the other using laser light. The opportunity to test the neural network on these three distinct systems gave them the chance to look for differences in how the neural network operated on each platform and to get a more complete picture of how the quantum neural network performed.

“We showed that for this architecture, you can see improvement in classification of images when you tune the quantumness from zero to some golden spot,” says Djamil Lakhdar-Hamina, a JQI graduate student and the first author of the paper.

The neural network performed slightly differently on each quantum computer. On all the platforms, the results showed that a limited amount of randomness from the quantum measurements improved the neural network’s performance compared to cases with no intentional randomness, but at some point, additional randomness made the neural network perform worse.

This outcome wasn’t completely surprising. A little bit of randomness is known to be a useful ingredient in machine learning—although it is normally incorporated in a different way and at a different time. This is because perfectly following a single fixed path inevitably gets the same answer every time, which can be a problem. Without any randomness to provide wiggle room in exploring all the options, a computer program or the training of a neural network is more likely to get stuck at a wrong answer that is almost correct. For instance, attempts to identify handwritten digits may mistake a sloppy seven as a one and lock onto that wrong answer. By contrast, a similar approach with some randomness to shake things up can often break free of the almost right answer and move on to the correct one.

When researchers develop neural networks, they often incorporate randomness into the training. In this experiment, the team looked for a benefit from using quantum measurements to inject randomness during the actual identification. The team saw that there were certain images that the neural network falsely identified when there was no quantum randomness, but it identified more images correctly when just the right amount of randomness was added to the quantum measurements. The group focused on these troublesome cases and did repeated experiments using a particular image that the neural network identified incorrectly when they didn’t add randomness. They showed that with the optimal amount of randomness added, the trapped-ion quantum computer could almost always identify the number and IBM’s quantum computer succeeded around 90% of the time.

Development Opportunities

The researchers also considered sources of randomness beyond what they introduced using quantum measurements. Quantum computers all experience noise—random errors from things like heat fluctuations that throw off their operations, and the group looked for signs of these errors influencing their results. The errors in quantum computers are currently a significant impediment to putting them to useful work, and researchers are looking for the most practical problems that can be tackled with imperfect devices that we have or expect to build soon.

“We're living in the era of what's called NISQ—noisy intermediate scale quantum computing,” Lakhdar-Hamina says. “So the question is, rather than this being a bad thing, can we harness the noise to positive ends?”

Based on the experiment, the researchers hope that machine learning is an area where quantum noise can be a benefit and not just a hindrance. They saw a couple of signs of the extra randomness being beneficial. The fact that the results varied a little when the team ran identical versions of the neural network on each computer suggests the imperfections of each are playing a noticeable role. Additionally, the researchers saw better results in the experiments both with and without randomness from measurements than they expected from their simulations of how the neural network would perform. The team attributed these results to the different imperfections and noise of each computer and concluded that the noise helped by contributing some amount of useful randomness.

The team’s results suggest that both intentional quantum randomness from measurements and the noise of quantum computers might serve a useful role in some neural networks. Even with these encouraging results, the researchers don’t propose that their current neural network design will produce the best possible neural network or even the best quantum neural network. However, the ability to tune the quantum randomness provides a tool to study the effect it plays and to learn how quantum computers might effectively be used with future neural networks.

Moving forward, they plan to develop a neural network that lets them explore even more quantum effects. In particular, they’re interested in exploring the potential benefit of utilizing entanglement, which links quantum particles together independent of their separation in space. They hope these neural networks will help them identify cases where quantum computing and machine learning can boost each other’s potential and demonstrate their combined power to solve challenging problems.

“Machine learning is not only socially and economically important, but is a fascinating part of computer science,” Lakhdar-Hamina says. “I think that it is one of the rare instances where we might actually find quantum advantage. And so this was a first step in what I think is going to be a big project for many, many people trying to find that quantum advantage within machine learning.”

Original story by Bailey Bedford: https://jqi.umd.edu/news/researchers-explore-how-quantum-computers-and-their-errors-may-enhance-ai

In addition to Galitski, Linke, Green and Lakhdar-Hamina, co-authors of the paper include former JQI graduate students Richard Barney and Xingxin Liu and Sarah Miller, who is a Research Scientist at UMD's Applied Research Laboratory for Intelligence & Security and an expert on machine learning and artificial intelligence.

A New Kind of Entanglement Helps Quantum Sensors Tune Out Noise

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Published: Wednesday, June 17 2026 01:34

In a quest to build the most accurate sensors in the world, scientists are constantly improving their performance. Making them more precise, stable and reliable. Photon exchange through an optical cavity links two atomic ensembles, creating a shared entangled state. This entanglement is designed to be insensitive to common noise while remaining highly sensitive to differential signals. (Credit: Raphael Kaubruegger, JILA)

But eventually, physical constraints will prevent further improvements. 

“By fully embracing the laws of quantum physics, one can expand the performance limits imposed by these constraints,” says JQI Fellow Alexey Gorshkov, who is also a Physicist at the National Institute of Standards and Technology (NIST), a Fellow of the Joint Center for Quantum Information and Computer Science and an Associate Professor in the Department of Physics at the University of Maryland. “And it's very exciting to come up with protocols that come as close as possible to saturating these limits for different sensing tasks.”

Even the most precise sensors in the world are not fully isolated and are limited by noise—subtle disturbances from the environment like vibrations, electromagnetic fields or temperature changes. 

So, Gorshkov, JILA Fellows Ana Maria Rey and James K. Thompson and their colleagues from the Niels Bohr Institute and the Indian Institute of Technology Madras, asked, how can we improve the next generation of sensors despite these limitations? 

One promising idea is to use quantum entanglement, so atoms are connected to each other and working together as a system to form a quantum sensor. When atoms are entangled, they share properties even when separated by distance. In principle, this allows for more precise measurements. But entangled atoms are still subject to noise. “Entangled states are well understood for estimating a single parameter, but our goal was to create an entangled state that is highly sensitive to a parameter difference between two nodes of a sensor network,” says Raphael Kaubruegger, a research associate at JILA and the lead author of the article. 

The researchers set out to identify a new class of entangled states that could filter out noise affecting both sensors. They then developed two ways to create these states inside an optical cavity, a pair of mirrors about one inch apart that bounce photons back and forth. They describe the state and two methods to create it in a recent paper published in Physical Review X. 

The entangled state they identified uses decoherence-free subspaces which are protected from certain types of disturbances to quiet noise affecting both sensors. 

Lasers are used to create coherent superposition between two internal states of an atom, but to accomplish that, the laser’s frequency needs to exactly match the atomic transition. 

The challenge, as Rey explains, is that even the most precise lasers cannot maintain a stable frequency for long enough. These laser frequency instabilities generate noise which is equally experienced by both sensors and is currently one of the most detrimental errors in state-of-the-art clocks. “Ideally, one would like to prepare the atoms in a state that is insensitive to this type of noise,” says Rey, who is also a NIST fellow and professor adjoint of physics at the University of Colorado Boulder. 

“The state we create is entanglement between these atoms, but in a way that you cannot distinguish which atom is in which ensemble,” Rey says. “They are fully symmetrized.” 

“After the fact, we realized this was the same kind of state people were thinking about to describe antiferromagnets, or quantum magnets,” says Thompson, who is also NIST fellow and professor adjoint of physics at the University of Colorado Boulder. 

In condensed matter physics, the Lieb-Mattis state describes a quantum version of an antiferromagnet, where two groups of atoms act like they point in opposite directions, but without the system picking one fixed direction in space. 

One method the team developed to prepare the desired state involves entangling two nodes of a sensor network by engineering a “spin exchange,” by having the atoms send photons back and forth through an optical cavity. This leads to a state where each atom in one node is perfectly anticorrelated with an atom in the other. If one atom is “up,” the other atom is “down.” 

Thompson likens this approach to baseball, where each ensemble is a baseball team. The teams are throwing balls, or in this case photons, to each other. Every time a ball is thrown, the other team catches it. Thompson adds that it’s important that we don’t know which player threw the ball or who caught it. 

“That’s what builds these links,” Thompson says. “If a ball is thrown, it is definitely caught.” 

The approach produces Heisenberg scaling, or the best possible precision scaling where all the atoms act as one quantum object. 

Optical cavities are not perfect. As Rey explains, sometimes you may lose a photon. The team’s second approach takes this into account. 

Inside the optical cavity, photons can bounce back and forth between very reflective mirrors about 100,000 times before they accidentally slip through to the other side. 

“We are losing photons, but the important part is that the photons are lost in a collective way,” Rey says. 

Because it’s impossible to tell which atom is to blame, this can create entanglement—driving them into a state where they cannot lose more photons. 

“At some point they get really good at not dropping the ball anymore,” Thompson says. 

“They go into a ‘dark state,’ or a state where the phases of the emitted photons completely cancel out, leading to what it is known as destructive interference,” Rey adds. 

The team was initially trying to understand the detrimental effect of losing those photons. But as Rey explains, ultimately this type of dissipation actually led them to a state they wanted. 

“The state we initially wanted to prepare was one in which half the atoms are excited, but the system cannot collectively emit a photon,” Kaubruegger adds. 

The team’s proposed states can be created quickly, and more importantly, faster as the system gets larger, making them practical for scaling quantum sensors. 

“People have thought about this kind of state when you only have two atoms, which is cool, but you’d like to use more,” Thompson says. “It turns out, the more atoms you have, the better!” 

By making quantum sensors more precise, these entangled states could one day help guide navigation when GPS is unavailable or reveal hidden underground resources such as minerals, oil or gas. 

Close collaborations between theorists and experimentalists have been key to this work. The groups inspire each other—and keep each other in check. Because they work so closely together, Kaubruegger says they have a deeper understanding of the challenges experimentalists face. 

And now, the ball, so to speak, is in Thompson’s group’s hands; to demonstrate the state in experiment.

This text has been adapted with permission from a story written by Kirsten Apodaca and originally published by JILA. It has been adapted with minor changes here.

Lepton Flavor Universality Tests Using Bc+ Decays at LHCb

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Published: Tuesday, May 19 2026 07:04

UMD graduate student Emily Jiang delivered a CERN seminar on May 19, 2026, unveiling an important new result on studies of Lepton Flavor Universality using decays of the heaviest B meson, B_c⁺, which has quark contents of a bottom-quark and anti-charm quark.   

The result is primarily the work of Jiang, UMD alumnus Zishuo Yang (Ph.D., 2023), Phoebe Hamilton and Hassan Jawahery, members of the Large Hadron Collider beauty experiment (LHCb) at CERN in Geneva, Switzerland.  It was a seven-year effort undertaken while the UMD group was also working on the development and construction of the new LHCb detector.LHCb

These results are of significant interest in the field because they show deviation from the Standard Model predictions. Previous measurements of similar quantities using the light B mesons from the BaBar experiment at SLAC, Belle Experiment at the KEK laboratory in Japan and the LHCb experiment at CERN are also in tension with the Standard Model. The new results show a similar trend, reinforcing the effect and has been highly anticipated in the field.

Jiang’s presentation can be seen here: https://indico.cern.ch/event/1685950/attachments/3277204/5855756/26-04-23_ejiang_CERN_seminar.pdf

For further information:

https://bolek.web.cern.ch/RJpsi/

https://lhcb-outreach.web.cern.ch/2026/05/19/lepton-flavor-universality-tests-using-bc-decays-at-lhcb/

Sudden Breakups of Monogamous Quantum Couples Surprise Researchers

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Published: Monday, January 05 2026 01:40

Quantum particles have a social life, of a sort. They interact and form relationships with each other, and one of the most important features of a quantum particle is whether it is an introvert—a fermion—or an extrovert—a boson.

Extroverted bosons are happy to crowd into a shared quantum state, producing dramatic phenomena like superconductivity and superfluidity. In contrast, introverted fermions will not share their quantum state under any condition—enabling all the structures of solid matter to form.An exciton forms when an electron pairs up with a hole—a mobile particle-like void in a material where an electron is missing from an atom. When paired up as an exciton, a hole and electron normally travel around together as an exclusive couple, but a new experiment probes what happens when conditions in a material break up the pair. In the image, a hole (grey sphere) resides in the bottom layer of a stacked material and is paired to an electron in the top layer (cyan sphere). None of the electrons present in the top layer (black spheres) are willing to share a spot in the material with each other or the electron in the exciton. (Credit: Mahmoud Jalali Mehrabad/JQI)

But the social lives of quantum particles go beyond whether they are fermions or bosons. Particles interact in complex ways to produce everything we know, and interactions between quantum particles are key to understanding why materials have their particular properties. For instance, electrons are sometimes tightly locked into a relationship with a specific atom in a material, making it an insulator. Other times, electrons are independent and roam freely—the hallmark of a conductor. In special cases, electrons even pair up with each other into faithful couples, called Cooper pairs, that make superconductivity possible. These sorts of quantum relationships are the sources of material properties and the foundations of technologies from the simplest electrical wiring to cutting-edge lasers and solar panels.

Professor and JQI Fellow Mohammad Hafezi and his colleagues set out to investigate how adjusting the ratio of fermionic particles to bosonic particles in a material can change the interactions in it. They expected fermions to avoid each other as well as the bosonic counterparts chosen for the experiment, so they predicted that large crowds of fermions would get in the way and prevent bosons from moving far. The experiment revealed the exact opposite: When the researchers attempted to freeze the bosons in place with a barricade of fermions, the bosons instead started traveling quickly.

“We thought the experiment was done wrong,” says Daniel Suárez-Forero, a former JQI postdoctoral researcher who is now an assistant professor at the University of Maryland, Baltimore County. “That was the first reaction.”

But they went on to thoroughly check their results and eventually came up with an explanation. The researchers shared their experiments and conclusions in an article published on Jan. 1, 2026 in the journal Science. They had stumbled onto a way to host a quantum party where the particles throw their social norms out the window, producing a dramatic—and potentially useful—change in behavior.

The group’s experiment explored the interactions electrons have with each other and with couples formed from an electron and a hole. Holes aren’t quite real particles like electrons. Instead, they are quasiparticles—they behave like particles but only exist as a disturbance of the surrounding medium. A hole is the result of a material missing an electron from one of its atoms, leaving an uncompensated positive charge. The hole can move around and carry energy like a particle within the material, but it can never leave the host material. And if an electron ever falls into a hole, the hole disappears. 

Sometimes, electrons and holes form an atom-like arrangement (with the hole playing the role of a proton). When this happens, the hole and electron move together and behave like a single quantum object that researchers call an exciton. It normally takes energy to break up the particles in an exciton, so as an exciton moves the hole and electron pretty much always stick together. This fact led physicists to label the exciton relationship as “monogamous.” 

The composite excitons are bosons, while individual electrons are fermions. Together, the two provided a suitable cast for the group’s experiments on fermion and boson interactions.

“At least this was what we thought,” said Tsung-Sheng Huang, a former JQI graduate student of the group who is now a postdoctoral researcher at the Institute of Photonic Sciences in Spain. “Any external fermion should not see the constituents of the exciton separately; but in reality, the story is a little bit different.”

To get the particles they needed and a suitable way to control them, the researchers created a material with the qualities they needed for their experiment by carefully aligning a layer of one thin material on another thin material with just the right alignment. The material’s properties allowed them to easily create excitons that live for a relatively long time, while its structure kept things orderly by providing a neat grid of spots where an exciton or an unpartnered electron need to reside.

Because of the structure, the electrons and excitons don’t see the material as a standing-room-only concert venue but, instead, as a restaurant set up for Valentine’s Day—all the floor space is crammed with small, intimate tables. In the material, every exciton and lone electron needs to be sat at a table, and the introverted solo electrons won’t share—either with each other or with an exciton. 

However, excitons generally aren’t content to stay in their original seats. They tend to move around. But instead of brazenly walking across the room, an exciton surreptitiously hops from one adjacent empty table to the next—sometimes resulting in an inefficient detour around a cluster of occupied tables.

During an experiment, the researchers can host trillions of particles in the material’s seating plan, and they can control the number of excitons and electrons that are free to move through the room. To add or remove electrons, the researchers apply different electrical voltages, which can force electrons into or out of the material. To add excitons, they summon them from the existing material. The researchers can shine a specific color of laser on the material, and its atoms will absorb the light. The energy from the laser knocks electrons loose from the atoms and creates excitons. 

The top half of the image shows the layered structure of a material that can host free-moving electrons (the black spheres) and excitons made of a hole (white sphere) partnered with a particular electron (cyan sphere). The bottom of the image shows the quantum landscape created by the material for the electrons and excitons. It contains many distinct locations where the electrons and excitons want to reside. The exciton can move to nearby empty spots but not one already occupied by an electron. (Credit: Mahmoud Jalali Mehrabad/JQI)

The researchers were able to track where the excitons they created end up; they just watched for the signs of their eventual destruction. When an exciton’s electron and hole eventually combine, the extra energy it carried must go somewhere, and it is commonly emitted as light. The researchers collected this light and used it as a marker of the final positions of the excitons. This let them determine how much each cluster of excitons diffused through the material even though they don’t watch their individual journeys.

“We can basically do any ratio,” Suárez-Forero says. “We can populate the system with only bosons, only fermions, or any ratio. And the diffusivity, the way in which the bosons move, changes a lot depending on the number of particles of each species.”

In the experiment, the researchers systematically adjusted the electron density and deduced what they could from the resulting changes in the diffusion of the bosons. They used the movement of the excitons as an indication of their interactions with the electrons and each other, turning each group of excitons into an experimental sensor.

When there were very few electrons, the researchers expected electrons to essentially never come across each other and thus to not have much influence on each other or the excitons. In contrast, abundant electrons are expected to avoid each other and to get in the way of the excitons.

Things started out as expected with the excitons traveling shorter and shorter distances as the electron population was dialed up. The excitons increasingly had to find a winding path around electrons instead of taking a mostly straight path.

Eventually, the experiment reached the point where almost every table was occupied by an electron. The researchers expected this to essentially halt exciton diffusion, but instead, they observed a sudden jump in the mobility of the excitons. Despite the fact that the excitons should have had their paths blocked, the distance they moved dramatically increased.

“No one wanted to believe it,” says Pranshoo Upadhyay, a JQI graduate student and the lead author of the paper. “It’s like, can you repeat it? And for about a month, we performed measurements on different locations of the sample with different excitation powers and replicated it in several other samples.”

They even tried the experiment in a different lab when Suárez-Forero concluded his postdoctoral work at JQI and spent some time as a research scientist at the University of Geneva.

“We repeated the experiment in a different sample, in a different setup, and even in a different continent, and the result was exactly the same,” Suárez-Forero says.

They also had to check that they weren’t misinterpreting the results. They were only seeing the exciton diffusion, not actually watching the interactions. They were relying on mathematical theories to explain the results, and they needed to make sure a mistake wasn’t hiding in their math.

The team formed a strong theoretical and experimental collaboration to figure out what was going on. 

“We spent months going back and forth with theorists, trying out different models, but none of them captured all our experimental observations,” Upadhyay says. “Eventually we realized that the excitons sit differently than the free electrons and holes in our system. That was the turning point—when we began thinking of the exciton beyond monogamy.”

The team concluded that the very crowded conditions were making the excitons give up on monogamy, so the researchers described the phenomenon as “non-monogamous hole diffusion.” Essentially, the surprising result occurred when the experimenters flooded the material—the metaphorical restaurant—with a bunch of electrons, each claiming a table to itself. The researchers determined that when the population of available electrons got sufficiently lopsided, the holes in each exciton saw all the other electrons as identical to the one they were already with; the normal rule of exciton monogamy broke down.

The rapid diffusion was caused by holes suddenly ditching their long-term electron partners. Instead of each working its way from table to table with the same electron, the holes were doing a speed dating round with electron after electron—allowing each exciton to make a beeline to its destination. Without the normal winding path around all the single electrons, each exciton travelled much farther before giving off its signature flash of destruction.

All the researchers needed to do to trigger this lopsided dating pool and rapid travel was adjust the voltage. Controlling voltages is no problem for existing devices, so the technique has broad potential to be conveniently integrated into future experiments and technologies that exploit excitons, like certain solar panel designs.

The researchers are already using this insight into how excitons and electrons can interact to interpret other experiments. They are also working to apply their new understanding of these materials to achieve greater control of the quantum interactions that they can induce in experiments.

“Gaining control over the mobility of particles in materials is fundamental for future technologies,” Suárez-Forero says. “Understanding this dramatic increase in the exciton mobility offers an opportunity for developing novel electronic and optical devices with enhanced capabilities.”

Original story by Bailey Bedford: https://jqi.umd.edu/news/sudden-breakups-monogamous-quantum-couples-surprise-researchers 

In addition to Hafezi, who is also a Minta Martin professor of electrical and computer engineering and physics at the University of Maryland and a senior investigator at the National Science Foundation Quantum Leap Challenge Institute for Robust Quantum Simulation; Upadhyay; Suárez-Forero and Huang, co-authors of the paper include JQI graduate students Beini Gao and Supratik Sarkar; former JQI postdoctoral researcher Deric Session who is now a systems scientist at Onto Innovation; Mahmoud Jalali Mehrabad, a former JQI postdoctoral researcher who is now a research scientist at MIT; Kenji Watanabe and Takashi Taniguchi, who are researchers at the National Institute for Material Science in Japan; You Zhou, who is an assistant professor at the University of Maryland’s School of Engineering; and Michael Knap, who is a professor at the Technical University of Munich in Germany.

This research was funded in part by the National Science Foundation and the Simons Foundation.