Lorraine DeSalvo Chair's Endowed Award for Outstanding Service

Upon Lorraine DeSalvo's 2019 retirement, the Department of Physics commended her 41 years of service by establishing the Lorraine DeSalvo Chair's Endowed Award for Outstanding Service. The award will recognize employees in the Department of Physics who provide benefit beyond their regular duties, promote positive professional and personal exchanges among colleagues and work effectively with internal and external partners. The chair of the Department of Physics will administer the fund and select recipients.

Contibutions can be made online or by check.  Checks should be made payable to the "University of Maryland College Park Foundation" or  "UMCP Foundation."  In the notes/for section on the check please write, "Lorraine DeSalvo Chair's Endowed Award."  Mail to: 

CMNS Office of the Dean
2300 Symons Hall 
University of Maryland
College Park, MD 20742

Read more about support for the Department of Physics here.

Joseph Sucher, 1930-2019

Professor Emeritus Joe Sucher, a UMD faculty member for 41 years, died on Oct. 18 at the age of 89.  A memorial was planned for Sunday, March 15, 2020, but was postponed indefinitely due to coronavirus concerns.  In commemoration of Joe's 90th birthday in September, 2020, his son Anatol produced a memorial program, which can be downloaded here.

Joe joined UMD in 1957, after earning his Ph.D. from Columbia University with a thesis on the quantum electrodynamics of the helium atom. He is best known for work on the relativistic theory of many-electron atoms, the quantum theory of long-range forces, the foundations of relativistic quantum theory, the Gellman-Low-Sucher level-shift formula, the no-pair Hamiltonian for many-electron atoms, the Levy-Sucher identity, the Dirac-Sucher equation and the Feinberg-Sucher formula for the long-range force between neutral atoms. He was a devoted educator and was named a UMD Distinguished Scholar-Teacher in 1989.

Two years ago, he established the Joseph and Dorothy Sucher Graduate Prize in Relativistic Theoretical Physics to remember Dorothy, his wife of 58 years. She was a psychotherapist and a writer for the Greenbelt News Review whose work resulted in a landmark Supreme Court decision upholding freedom of the press. Her last writing project took her to Russia and Belarus shortly after the fall of the Soviet Union, as she tried to piece together the history of her grandparents before they emigrated to the U.S. After her death, Joe and his son Anatol completed the work, Return to the Shtetl.

A native of Vienna, Joe was forced with his family to flee Hitler’s Nazism.  He escaped from Austria in 1938, and after a harrowing trek though Germany, Luxembourg, France, Spain and Portugal, arrived in the United States at age 10. He described the odyssey in a 2014 oral history interview with the United States Holocaust Memorial Museum.

Joe was well-known in the department for his great wit, his unfailing charm and his memorable lyrics; he often graced departmental gatherings with impressive poems, such as one he wrote on the 50th anniversary of the tradition of Physics Tea.

     

 

 

 

 

 

Hybrid Device among First to Meld Quantum and Conventional Computing

Researchers at the University of Maryland (UMD) have trained a small hybrid quantum computer to reproduce the features in a particular set of images.

The result, which was published Oct. 18, 2019 in the journal Science Advances, is among the first demonstrations of quantum hardware teaming up with conventional computing power—in this case to do generative modeling, a machine learning task in which a computer learns to mimic the structure of a given dataset and generate examples that capture the essential character of the data.

“We combined one of the highest performance quantum computers with one of the most powerful AI programs—over the internet—to form a unique kind of hybrid machine,” says Joint Quantum Institute (JQI) Fellow Norbert Linke, an assistant professor of physics at UMD and a co-author of the new paper.

The researchers used four trapped atomic ions for the quantum half of their hybrid computer, with each ion representing a quantum bit, or qubit—the basic unit of information in a quantum computer. To manipulate the qubits, researchers punch commands into an ordinary computer, which interprets them and orchestrates a sequence of laser pulses that zap the qubits.Close-up photo of an ion trap. Credit: S. Debnath and E. Edwards/JQIClose-up photo of an ion trap. Credit: S. Debnath and E. Edwards/JQI

The UMD quantum computer is fully programmable, with connections between every pair of qubits. “We can implement any quantum function by executing a standard set of gates between the qubits,” says JQI and Joint Center for Quantum Information and Computer Science (QuICS) Fellow Christopher Monroe, a physics professor at UMD who was also a co-author of the new paper. “We just needed to optimize the parameters of each gate to train our machine learning algorithm. This is how quantum optimization works.”

Monroe, Linke and their colleagues trained their computer to produce an output that matched the “bars-and-stripes” set, a collection of images with blocks of color arranged vertically or horizontally to look like bars or stripes—a standard dataset in generative modeling because of its simplicity.

“Machine learning is generally categorized into two types,” says Daiwei Zhu, the lead author of the paper and a graduate student in physics at JQI. “One enables you to tell whether something is a cat or dog, and the other lets you generate an image of a cat or dog. We’re performing a scaled-back version of the latter task.”

Turning the hybrid system into a properly trained generative model meant finding the laser sequence that would turn a simple input state into an output capable of capturing the patterns in the bars-and-stripes set—something that qubits could do more efficiently than regular bits. “In essence, the power of this lies in the nature of quantum superposition,” says Zhu, referring to the ability of qubits to store multiple states—in this case, the entire set of bars-and-stripes images with four pixels—simultaneously.

Through a series of iterative steps, the researchers attempted to nudge the output of their hybrid computer closer and closer to the quantum bars-and-stripes state. They began by preparing the input qubits, subjecting them to a random sequence of laser pulses and measuring the resulting output. Those measurement results were then fed to a conventional, or “classical,” computer, which crunched the numbers and suggested adjustments to the laser pulses to make the output look more like the bars-and-stripes state.

By adjusting the laser parameters and repeating the procedure, the team could test whether the output eventually converged on the desired quantum state. They found that in some cases it did, and in some cases it didn’t.

The researchers studied the convergence using two different patterns of connectivity between qubits. In one, each qubit was able to interact with all the others, a situation that the team called all-to-all connectivity. In a second, a central qubit interacted with the other three, none of which interacted directly with one another.  They called this star connectivity. (This was an artificial constraint, as the four ions are naturally able to interact in the all-to-all fashion. But it could be relevant to experiments with a larger number of ions.)

The all-to-all interactions produced states closer to bars-and-stripes after training short sequences of pulses. But the experimenters had another setting to play with: They also studied the performance of two different number crunching methods used on the conventional half of the hybrid computer.

One method, called particle swarm optimization, worked well when all-to-all interactions were available, but it failed to converge on the bars-and-stripes output for star connectivity. A second method, which was suggested by three researchers at the Oxford, UK AI company Mind Foundry Limited, proved much more successful across the board.

The second method, called Bayesian optimization, was made available over the internet, which enabled the researchers to train sequences of laser pulses that could produce the bars-and-stripes state for both all-to-all and star connectivity. Not only that, but it significantly reduced the number of steps in the iterative training process, effectively cutting in half the time it took to converge on the correct output.

“What our experiment shows is that a quantum-classical hybrid machine, while in principle more powerful than either of the components individually, still needs the right classical piece to work,” says Linke.  “Using these schemes to solve problems in chemistry or logistics will require both a boost in quantum computer performance and tailored classical optimization strategies.”

Story by Chris Cesare 

In addition to Linke, Monroe and Zhu, co-authors of the research paper include University College London computer science student Marcello Benedetti; JQI physics graduate students Nhung Hong Nguyen, Cinthia Huerta Alderete and Laird Egan and recent JQI Ph.D. graduate Kevin Landsman; Zapata Computing scientist Alejandro Perdomo-Ortiz; Mind Foundry Limited scientists Nathan Korda, Alistair Garfoot and Charles Brecque; and Central Connecticut State University Mathematical Sciences Professor Oscar Perdomo.

Research Contacts:
Daiwei Zhu  This email address is being protected from spambots. You need JavaScript enabled to view it.
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Media Contact:
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Stretched Photons Recover Lost Interference

The smallest pieces of nature—individual particles like electrons, for instance—are pretty much interchangeable. An electron is an electron is an electron, regardless of whether it’s stuck in a lab on Earth, bound to an atom in some chalky moon dust or shot out of an extragalactic black hole in a superheated jet. In practice, though, differences in energy, motion or location can make it easy to tell two electrons apart.

One way to test for the similarity of particles like electrons is to bring them together at the same time and place and look for interference—a quantum effect that arises when particles (which can also behave like waves) meet. This interference is important for everything from fundamental tests of quantum physics to the speedy calculations of quantum computers, but creating it requires exquisite control over particles that are indistinguishable.

Researchers recorded these patterns of quantum interference between three photons that started out as separate, distinguishable particles.Researchers recorded these patterns of quantum interference between three photons that started out as separate, distinguishable particles.

With an eye toward easing these requirements, researchers at the Joint Quantum Institute (JQI) and the Joint Center for Quantum Information and Computer Science (QuICS) have stretched out multiple photons—the quantum particles of light—and turned three distinct pulses into overlapping quantum waves. The work, which was published recently in the journal Physical Review Letters, restores the interference between photons and may eventually enable a demonstration of a particular kind of quantum supremacy—a clear speed advantage for computers that run on the rules of quantum physics.

“While photons do not directly interact with each other, when they meet they can exhibit a purely quantum feature absent from classical, non-quantum waves,” says JQI Fellow Mohammad Hafezi, a co-author of the paper and an associate professor of physics and electrical and computer engineering at the University of Maryland.

These days, testing the similarity of photons is routine. It involves bringing them together at a device called a beam splitter and measuring the light coming out the other side.

When a single photon hits a balanced beam splitter, there’s a 50 percent chance that it will travel straight through and a 50 percent chance that it will reflect off at an angle. By placing detectors in these two possible paths, scientists can measure which way individual photons end up going.

If two identical photons meet at the beam splitter, with one traveling to the east and the other to the north, it’s tempting to apply the same treatment to each particle individually. It’s true that both photons have an equal chance to travel through or reflect, but because the photons are indistinguishable, it’s impossible to tell which one goes where.

The upshot of this identity confusion is that two of the possible combinations—those in which both photons travel straight through the beam splitter and both photons reflect—cancel each other out, leaving behind a distinctly quantum result: The photons team up and travel as a pair, always ending up at one of the two detectors together.

Now Hafezi and his colleagues from UMD and the University of Portsmouth have observed a similar interference effect with distinguishable photons—pulses of light just two picoseconds long (a picosecond is a trillionth of a second) that are separated by tens of picoseconds. The essential trick was finding a way to make the pulses less distinguishable so that they could interfere.

“We used a single optical element that’s basically a fiber,” says Sunil Mittal, a postdoctoral researcher at JQI and a co-author of the new paper. “It emulates the equivalent of about 150 kilometers of fiber, which stretches the photons. It acts a bit like a lens in reverse, causing different frequencies in the pulses to disperse and defocus.”

By lengthening each photon by a factor of about 1000, the researchers could effectively erase the time delay between pulses and create large sections of overlap. That overlap made it more likely that photons would arrive to detectors at the same time and interfere with one another.

Prior experiments (including by JQI and QuICS Fellow Christopher Monroe and collaborators) have successfully interfered distinguishable photons, but those results required multiple channels for the incoming light—one for each photon. The new work uses just a single channel that carries light at standard telecom frequencies, which the authors say allows their system to easily scale to include many more photons.

Having more photons would allow researchers to study boson sampling, a computational problem that’s thought to be too hard for ordinary computers (similar to the problem Google is rumored to have solved). In its standard form, boson sampling concerns photons—which are members of a family of particles called bosons—making their way through a big network of beam splitters. The photons enter the network through different channels and exit to detectors, with one detector per channel.

The boson sampling “problem” amounts to doing a complicated coin flip, since each experiment samples from the underlying chance that (say) three photons entering the network at ports 1, 2 and 5 will end up at outputs 2, 3 and 7. The interference inside the network is complex and impossible to track with a regular computer—even for modest numbers of photons—and it gets harder the more photons you add. But with real photons in a real network, the problem would solve itself.

“The connection of this experiment to boson sampling is a great example of how the growing synergy between quantum many-body physics and computational complexity theory can lead to great progress in both fields,” says JQI and QuICS Fellow Alexey Gorshkov, an adjunct associate professor of physics at UMD and another co-author of the paper.

But up until now, boson sampling experiments have suffered from the problem of scalability: Solving the problem for more photons meant adding more channels, which meant taking up more space and timing the arrival of yet more photons to ensure their interference. Mittal says that their technique potentially solves both of these problems.

“In our system, the inputs don’t need to be in different fibers,” Mittal says. “All the photons can travel in a single fiber and the time differences can be erased by the same method we’ve already demonstrated.” Another off-the-shelf device could mimic the network of beam splitters, with the added benefit of allowing for easy reconfiguration, Mittal says. “We’re not doing boson sampling now, but it would be relatively easy to go in that direction.”

Story by Chris Cesare  This email address is being protected from spambots. You need JavaScript enabled to view it.

In addition to Hafezi, Mittal and Gorshkov, co-authors of the research paper include electrical and computer engineering graduate student Venkata Vikram Orre; JQI Research Scientist Elizabeth Goldschmidt, who is now an assistant professor of physics at the University of Illinois at Urbana-Champaign; physics graduate student Abhinav Deshpande; and Vincenzo Tamma, a physicist at the University of Portsmouth.