New Design Packs Two Qubits into One Superconducting Junction

Quantum computers are potentially revolutionary devices and the basis of a growing industry. However, their technology isn’t standardized yet, and researchers are still studying the physics behind the diverse ways to build these quantum devices. Even the most basic building blocks of a quantum computer—qubits—are still an active research topic.A superconducting circuit studied in Alicia Kollár’s lab. The middle of the three rectangles along the bottom are junctions that hold quantum states that may each be used as a qubit. A proposal to adjust the dimensions of the junctions would allow chips like this to host twice as many qubits.A superconducting circuit studied in Alicia Kollár’s lab. The middle of the three rectangles along the bottom are junctions that hold quantum states that may each be used as a qubit. A proposal to adjust the dimensions of the junctions would allow chips like this to host twice as many qubits.

In an article published September 23, 2024 in the journal Physical Review A, JQI researchers proposed a way to use the physics of superconducting junctions to let each function as more than one qubit. They also outlined a method to use the new qubit design in quantum simulations. While these proposed qubits might not immediately replace their more established peers, they illustrate the rich variety of quantum physics that remains to be explored and harnessed in the field.

Superconducting junctions are part of many diverse qubit designs, including those in the prototype quantum computers of IBM and Google. All the designs feature an island made of a superconductor joined to the rest of a superconducting circuit by a thin layer of insulator that forms the junction between the two sections. To cross the barrier, electrons in the circuit must quantum tunnel through the junction, influencing which quantum states the circuit can naturally hold.

JQI Fellow Mohammad Hafezi and JQI postdoctoral researcher Andrey Grankin, who is the first author of the paper, reviewed the research on junctions in superconducting circuits, and what they found left them wondering if the existing qubit designs were taking advantage of the full breadth of physics that can be realized in superconducting junctions. The design of a junction—the geography of the superconducting island—impacts which states it can host, and current designs have focused on small junctions and the simplest states.

In some qubit designs, the quantum states depend on the geography of the island because of how electrons in a superconductor are free to move around like a fluid. Like water in a small pool, the electrons can slosh back and forth and form waves that are influenced by their surroundings.

Certain electron waves isolated onto a superconducting island can be very stable and long lasting, which makes them useful for storing quantum information in a qubit. The waves that are stable are examples of a more general phenomenon, called standing waves, that occur when a wave isn’t interrupted during the slope of one of its hills or valleys; instead, its oscillations are perfectly completed at the edges (the walls of a pool, the points where a string is being held, etc.). A guitar string’s harmonics are also examples of standing waves. 

But just having a stable standing wave in the superconducting electrons isn’t enough to be useful for quantum calculations. To use a standing wave as a qubit, a quantum computer must be able to distinguish it from all other standing waves and individually target it. Current superconducting qubit designs circumvent this issue by using short junctions that host just a single standing wave; as long as a junction is sufficiently short, the physics governing the superconducting electrons effectively only allows a single standing wave on the superconducting island. Researchers have also studied junctions that meet along very long interfaces and found that they can easily host a vast array of standing waves. Unfortunately, the abundance of standing waves comes with a downside: The more standing waves there are, the more similar the waves become, which makes them difficult to tell apart and inconvenient for quantum computing. 

“Historically short and long junctions were researched quite extensively,” says Grankin, who is the first author of the paper. “But the intermediate junction lengths have not been studied.”

Hafezi—who is also a Minta Martin professor of electrical and computer engineering and physics at the University of Maryland (UMD) and a Senior Investigator at the National Science Foundation Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS)—and Grankin became interested in this intermediate regime. The pair consulted with JQI Fellow Alicia Kollár, another author of the paper who works with superconducting qubits in her research.

“Andrey and Mohammad came to me with a creative new idea for how to make a junction host multiple qubit excitations,” says Kollár, who is also a Chesapeake Assistant Professor of Physics and a Co-Associate Director of Research for RQS. “Our main challenge was coming up with a design that would yield practical device parameters and a device that is actually within reach of current state-of-the-art fabrication techniques.”

Together, the group explored the behaviors of electrons in the intermediate case and if it is practical to produce multiple excitations that can be easily distinguished and separately manipulated. Since the pool of electrons is within a solid superconductor, setting them into motion isn’t as simple as plucking a string. To push and pull on electrons you need an electric field. One way that physicists like to push electrons around is using a special reflective chamber—or resonator—that is full of electric and magnetic fields in the form of light. 

Light in a resonator can form its own standing waves that act on the electrons. Similar to a guitar string vibrating due to the sound waves from another string—or more dramatically the sound of a singer’s voice shaking a glass until it shatters—the right light waves inside a resonator can excite electrons in a superconducting junction into a standing wave, which physicists call a mode of the junction. 

The team analyzed how medium-sized junctions should behave inside a resonator and found promising results. The various modes of a junction each respond more or less strongly to particular frequencies of light, so light can be selected to target a specific mode. The response of a mode to a standing wave of light in a resonator also depends on whether the symmetries of the mode and the light match. If the waves of light in the resonator are symmetrical across the center of a junction, they naturally push electrons into waves with a similar symmetry. For instance, if the light waves crossing the junction form a hill on one side and a valley on the other, they can’t push the electrons into a simple hill reflected across the center of the device, but they might be able to excite a similarly lopsided mode of the junction. 

So, light that creates one mode in the superconducting electrons may be ignored by another mode. In the paper, the team described a method of exploiting these two effects to excite or manipulate only a targeted mode. The researchers proposed a design where two distinct modes are targeted so that a single junction functions as two independent qubits. They also described a method to use a one-dimensional line of junctions to simulate interactions between two-dimensional grids of quantum particles. However, they haven’t yet tackled fabricating the junctions and demonstrating the feasibility of their proposal.

“This project started from a fundamental interest in the electrodynamics of extended junctions,” Grankin says. “Then it turned out to be also useful from the quantum information and simulation perspective.”

Original story by Bailey Bedford: https://jqi.umd.edu/news/new-design-packs-two-qubits-one-superconducting-junction

HAWC Finds High-Energy Gamma-Ray Emissions from Microquasar V4641 Sagittarii

A new study in Nature, Ultra-high-energy gamma-ray bubble around microquasar V4641 Sgr,"   has  revealed a groundbreaking discovery by researchers from the High Altitude Water Cherenkov (HAWC) observatory:  TeV gamma-ray emissions from V4641 Sagittarii (V4641 Sgr), a binary system composed of a black hole and a main sequence B-type companion star. This discovery provides fresh insights into particle acceleration in large-scale jets emitted from microquasars, which serve as natural laboratories for studying high-speed jets produced by matter falling onto spinning black holes. The findings show that V4641 Sgr's gamma-ray emissions occur at similar distances from the black hole as those observed in another well-known microquasar, SS 433. This makes V4641 Sgr stand out for its super-Eddington accretion and one of the fastest superluminal jets in the Milky Way. With a gamma-ray spectrum that ranks among the hardest of any known TeV sources, the emissions are detected at energies exceeding 200 TeV. Schematic illustration of the V4641 Sgr region.Schematic illustration of the V4641 Sgr region.

The study's results indicate that the gamma rays are likely produced by extremely high-energy protons. While studies of SS 433 indicate that the emission from this object is likely electrons, the high energy emission at large distances from V4641 argue against electrons due to their rapid energy loss at high energies. The implications are profound: the environment around the microquasar may play a critical role in determining whether the emission from the large-scale jets comes from electrons or protons, and they could play a significant role as a source of Galactic cosmic rays. These observations open new avenues for understanding particle acceleration in extreme environments and contribute to the broader study of high-energy astrophysics. 

 "At a zenith angle of 46° from HAWC's field of view, the microquasar V4641 Sgr has accelerated particles to the knee of the cosmic-ray spectrum, pushing the boundaries of our understanding of particle acceleration and transport in such extreme environments," said Dr. Dezhi Huang, a Post-Doctoral researcher at the University of Maryland. "HAWC observatory continues to deliver exceptional performance, providing valuable insights into these high-energy processes that were previously beyond our reach. "

“The HAWC survey has discovered for the first time very high energy gamma rays from the extended 100 pc jet of the microquasar V4641 Sgr. This proves that jets launched in accreting systems can accelerate particles up to PeV energies, and therefore that microquasars are potentially significant contributors to the Galactic cosmic ray population at high energies," saidDr. Sabrina Casanova, Professor from Institute of Nuclear Physics of the Polish Academy of Sciences. "Furthermore, although very high energy protons are strongly suspected to exist in the kpc jets of active galactic nuclei (AGN), the associated hundred TeV emission has never been observed from an extended region from an AGN jet due to strong gamma-ray absorption over the long distances to Earth.” Differential spectrum weighted by E2 for the northern and southern sources in a model with two point sources and for the asymmetric extended source in a model with a single asymmetric extended source. The shaded regions indicate the best-fit spectra and 1σ statistical uncertainties when fitting a single-power-law model to the data from 10 to >200 TeV. The markers correspond to the best-fit values and their 1σ statistical uncertainties obtained when fitting a single-power-law model to data in individual energy bins. The chosen energy range for plotting the spectrum is specified in the Methods.Differential spectrum weighted by E2 for the northern and southern sources in a model with two point sources and for the asymmetric extended source in a model with a single asymmetric extended source. The shaded regions indicate the best-fit spectra and 1σ statistical uncertainties when fitting a single-power-law model to the data from 10 to >200 TeV. The markers correspond to the best-fit values and their 1σ statistical uncertainties obtained when fitting a single-power-law model to data in individual energy bins. The chosen energy range for plotting the spectrum is specified in the Methods.

This study was supported by the collaborative efforts of multiple institutions, with major contributions from the University of Maryland. Distinguished University Professor Jordan Goodman, a member of the collaboration's internal editorial board, played a key role in guiding the publication. Dr. Dezhi Huang, one of the corresponding authors, led significant aspects of the analysis, while Dr. Kristi Engel helped refine the paper. Additional support came from UMD HAWC group members Research Scientist Dr. Andrew Smith, Project Engineer Michael Schneider, Dr. Zhen Wang, Dr. Jason Fan and graduate students Sohyoun Yun-Cárcamo.

More on the finding: https://www.nature.com/articles/d41586-024-03191-x

Sasha Philippov Awarded 2024 Packard Fellowship

Assistant Professor Sasha Philippov has been named one of 20 members of the 2024 class of Packard Fellows for Science and Engineering. Sponsored by the David and Lucile Packard Foundation, the $875,000, five-year award for early-career researchers provides “flexible funding and the freedom to take risks and explore new frontiers in their fields of study,” according to the foundation.

Philippov is the seventh UMD faculty member—and the second from UMD’s Department of Physics—to receive this competitive award since its launch in 1988.

“I am delighted to see the recognition Sasha is receiving with the awarding of the Packard Fellowship,” said UMD Physics Chair Steven Rolston. “His outstanding work places our excellent plasma theory group at the center of multi-messenger astronomy, with multiple connections to efforts in physics and astronomy both within and beyond the university.”

Each year, the Packard Foundation invites 50 universities to nominate two faculty members for a Packard Fellowship, which is ultimately narrowed down to 20 recipients. Previous UMD awardees include Janice Reutt-Robey (chemistry and biochemistry) in 1990, William Pugh (computer science) in 1991, Victor Yakovenko (physics) in 1995, Victor Muñoz (formerly chemistry and biochemistry) and Sarah Tishkoff (formerly biology) in 2001, and Vedran Lekić (geology) in 2014.Sasha PhilippovSasha Philippov

Funding from the Packard Fellowship will enable Philippov to develop new computational codes capable of running on the world’s biggest supercomputers. In his research, Philippov uses computational astrophysics to study some of the most mysterious objects in the universe, including neutron stars and black holes. He is particularly interested in discovering how hot, magnetized gas—known as plasma—produces the light we see around exotic objects, such as the ring of light captured in the first image of a black hole in galaxy M87.

His new simulations would shed light on “how plasma shines around black holes,” as well as fast radio bursts—mysterious flashes of radio waves that are extremely bright and short-lived, lasting for mere milliseconds. Some of these extremely bright signals travel for billions of years before reaching Earth, but their exact origin remains an open question in astrophysics.

“Remarkable recent observational discoveries, including fast radio bursts and silhouettes of black holes, make it breathtaking and timely to work in this field,” Philippov said. “The common theoretical challenge to explaining stunning observations of neutron stars and black holes is understanding the behavior of relativistic plasma, the hot, magnetized, collisionless gas of charged particles producing the observed light under extreme conditions that we cannot explore on Earth.”

Simulations can complement images captured by the Event Horizon Telescope and other observatories, enabling researchers like Philippov to explore the physics of the highly energized electrons in plasma. He expressed gratitude to the Packard Foundation for supporting “high-risk, high-reward” research like the development of his new codes that could—if successful—yield much more realistic simulations.

“It could allow us to run three-dimensional kinetic simulations of black hole accretion, which we were not able to run before,” Philippov said.

Graduate students and postdoctoral researchers in Philippov’s lab will also play a hands-on role in developing this code, running and analyzing simulations, and constructing theoretical models of plasma phenomena.

Since joining UMD in 2022, Philippov has received a 2024 Sloan Research Fellowship, which provided him with $75,000 to study the production of neutrinos (weakly interacting particles) around black holes and magnetars (neutron stars with the strongest magnetic fields in the universe). He was also awarded a 2024 Thomas H. Stix Award for Outstanding Early Career Contributions to Plasma Physics Research for his “seminal contributions to the theory and simulation of collisionless astrophysical plasmas, especially compact objects.”

Although Philippov is excited to receive a Packard Fellowship, it is also bittersweet. Rolston and UMD Physics Professor Bill Dorland helped deliver the fellowship news to Philippov over a Zoom call last month, which ended up being the last time he and Dorland spoke.

Dorland, who was a mentor and friend to Philippov, died in September following a 20-year battle with chordoma, a rare cancer. In many ways, Philippov’s research will carry on Dorland’s legacy in computational plasma physics.

“Bill was a remarkable, kind and generous person. His passing left a giant void in all who had the privilege of knowing and working with him,” Philippov said. “He often mentioned that writing code is not just his job but a part of his very being. We will continue forging ahead in his memory.”

 

Original story by Emily C. Nunez: https://cmns.umd.edu/news-events/news/sasha-philippov-awarded-2024-packard-fellowship

Nobel Prize Celebrates Interplay of Physics and AI

On October 8, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey E. Hinton for their foundational discoveries and inventions that have enabled artificial neural networks to be used for machine learning—a widely used form of AI. The award highlights how the field of physics is intertwined with neural networks and the field of AI.(Credit: © Johan Jarnestad/The Royal Swedish Academy of Sciences)(Credit: © Johan Jarnestad/The Royal Swedish Academy of Sciences)

An artificial neural network is a collection of nodes that connect in a way inspired by neurons firing in a living brain. The connections allow a network to store and manipulate information. While neural network research is closely tied to the fields of neuroscience and computer science, it is also connected with physics. Ideas and tools from physics were integral in the development of neural networks for machine learning tasks. And once machine learning was refined into a powerful tool, physicists across the world, including at the University of Maryland, have been deploying it in diverse research efforts.

Hopfield invented a neural network, called the Hopfield network, that can work as a memory that stores patterns in data—this can be used for tasks like recognizing patterns in images. Each node in its network can be described as a pixel in an image, and it can be used to find an image in its memory from its training that most closely resembles a new image that it is presented to it. But the process used to compare images can also be described in terms of the physics that govern the quantum property of spin.

The spin of a quantum particle makes it behave like a tiny bar magnet, and when many magnets are near each other, they all work to orient themselves in specific ways (north poles repelled by the other north poles and attracted to south poles). Physicists can characterize a group of spins based on their interactions and the energy associated with the orientation all the spins want to fall into. Similarly, a Hopfield network can be described as characterizing images based on an energy defined by the connections between nodes.

Hinton used the tools of statistical physics to build on Hopfield’s work. He developed an approach to using neural networks called Boltzmann machines. The learning method of these neural networks fit the description of a specific type of spin orientation found in materials, called a spin glass. A Boltzmann machine can be used to identify characteristics of the data it was trained with. These networks can help classify images or create new elements that fit the pattern it has been trained to recognize.

Following the initial work of Hopefield and Hinton a broad variety of neural networks and machine learning applications have arisen. As neural networks have expanded beyond the forms developed by Hopfield and Hinton, they still resemble common physics models, and some physicists have chosen to apply their skills to understanding the large, often messy, models that describe neural networks.

“Artificial neural networks represent a very complicated, many-body problem, and physicists, especially condensed matter physicists, that's what we do,” says JQI Fellow Maissam Barkeshli, a theoretical physicist who has applied his expertise to studying artificial neural networks. “We study complex systems, and then we try to tease out interesting, qualitatively robust behavior. So neural network research is really within the purview of physicists.”

In a paper that Barkeshli and UMD graduate student Dayal Kalra shared at the Conference on Neural Information Processing Systems last year, the pair presented the results of their investigation of the impact of the learning rate—the size of steps that are made each time the network’s parameters are changed during training—on the optimization of a neural network. Their analysis predicted distinct behaviors for the neural network depending on the learning rate used.

“Our work focused on documenting and explaining some intriguing phenomena that we observed as we tuned the parameters of the training algorithm of a neural network,” Barkeshli says. “It is important to understand these phenomena because they deeply affect the ability of neural networks to learn complicated patterns in the data.”

Other similar questions in neural network research remain, including how the information is encoded in a network, and Barkeshli says that many of the open questions are likely to benefit from the perspectives and tools of physicists.

In addition to the field of machine learning benefitting from the tools of physics, it has also provided valuable tools for physicists to use in their research. Similar to the diverse uses of AI to play board games, create quirky images and write emails, the applications of machine learning have taken many forms in physics research.

“The laureates’ work has already been of the greatest benefit,” says Ellen Moons, Chair of the Nobel Committee for Physics. “In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”

Neural networks can also be useful for physicists who need to identify relevant data so they can focus their attention effectively. For instance, the scientific background document that was shared as part of the prize announcement cites the use of neural networks in the analysis of data from the IceCube neutrino detector at the South Pole. The project was a collaboration of many researchers, including UMD physics professors Kara Hoffman and Gregory Sullivan and UMD physics assistant professor Brian Clark, that produced the first neutrino image of the Milky Way.

Neutrinos are a group of subatomic particles that are notoriously difficult to detect since they have little mass and no electrical charge, which makes interactions uncommon (only about one out of 10 billion neutrinos is expected to interact as it travels all the way through the earth). The lack of interactions means neutrinos can be used to observe parts of the universe where light and other signals have been blocked or deflected, but it also means researchers have to work hard to observe them. The IceCube detector includes a cubic kilometer of ice that neutrinos can interact with. When an interaction is observed, researchers must determine if it was actually an interaction with a neutrino interaction or another particle. They must also determine the direction the detected neutrino came from and whether it likely originated from a distant source or if it was produced by particle interactions that occurred in the earth’s atmosphere.

UMD postdoctoral researcher Stephen Sclafani worked on the project as a graduate student at Drexel University. He was an author of the paper sharing the results and was a lead in the project’s use of machine learning in their analysis. Neural networks helped Sclafani and his colleagues select the desired neutron interactions observed by the detector from data that had been collected over ten years. The approach increased their efficiency at identifying relevant events and provided them with approximately 30 times the amount of data to use in generating their neutrino map of our galaxy.

“Initially, as many people were, I was surprised by this year's prize selection,” says Sclafani. “But neural networks are currently revolutionizing the way we do physics. The breakthroughs of Hopfield and Hinton are responsible for many other results and are grounded in statistical physics.”

Some physics research applications of neural networks draw more directly on the physics foundation of neural networks. Earlier this year, JQI Fellow Charles Clark and JQI graduate student Ruizhi Pan proposed new tools to expand the use of machine learning in quantum physics research. They investigated a type of neural network called a restricted Boltzmann machine (RBM)—a variation of Boltzmann machines with additional restrictions on the networks. Their research returned to the spin description of the network and investigated how well various numbers of nodes can do at approximating the state that results from the spin interactions of many quantum particles.

"We, and many others, thought that the RBM framework, applied by 2024 Nobel Physics Laureate Geoffrey Hinton to fast learning algorithms about 20 years ago, might offer advantages for solving problems of quantum spin systems," says JQI Fellow Charles Clark. "This proved to be the case, and the research on quantum models using the RBM framework is an example of how advances in mathematics can lead to unanticipated developments in the understanding of physics, as was the case for calculus, linear algebra, and the theory of Hilbert spaces."

There are numerous additional ways that neural networks are also being developed into valuable tools for physics research, and this year’s Nobel Prize in physics celebrates that contribution as well as its roots in the field.

For more information about the prize winners and their research that the award recognizes, see the press releases from the Royal Swedish Academy of Sciences.

Original story by Bailey Bedfod:  jqi.umd.edu/news/nobel-prize-celebrates-interplay-physics-and-ai

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