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.

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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.


Rare “Lazarus Superconductivity” Observed in Rediscovered Material

Researchers from the University of Maryland, the National Institute of Standards and Technology (NIST), the National High Magnetic Field Laboratory (National MagLab) and the University of Oxford have observed a rare phenomenon called re-entrant superconductivity in the material uranium ditelluride. The discovery furthers the case for uranium ditelluride as a promising material for use in quantum computers.

A team of researchers has observed a rare phenomenon called re-entrant superconductivity in the material uranium ditelluride. Nicknamed “Lazarus superconductivity,” the phenomenon occurs when a superconducting state arises, breaks down, then re-emerges in a material due to a change in a specific parameter—in this case, the application of a very strong magnetic field. The discovery furthers the case for uranium ditelluride as a promising material for use in quantum computers. Image credit: Emily Edwards/JQI (Click image to download hi-res version.)A team of researchers has observed a rare phenomenon called re-entrant superconductivity in the material uranium ditelluride. Nicknamed “Lazarus superconductivity,” the phenomenon occurs when a superconducting state arises, breaks down, then re-emerges in a material due to a change in a specific parameter—in this case, the application of a very strong magnetic field. The discovery furthers the case for uranium ditelluride as a promising material for use in quantum computers. Image credit: Emily Edwards/JQI (Click image to download hi-res version.)

Nicknamed “Lazarus superconductivity” after the biblical figure who rose from the dead, the phenomenon occurs when a superconducting state arises, breaks down, then re-emerges in a material due to a change in a specific parameter—in this case, the application of a very strong magnetic field. The researchers published their results on October 7, 2019, in the journal Nature Physics.

Once dismissed by physicists for its apparent lack of interesting physical properties, uranium ditelluride is having its own Lazarus moment. The current study is the second in as many months (both published by members of the same research team) to demonstrate unusual and surprising superconductivity states in the material.

“This is a very recently discovered superconductor with a host of other unconventional behavior, so it's already weird,” said Nicholas Butch, an adjunct assistant professor of physics at UMD and a physicist at the NIST Center for Neutron Research. “[Lazarus superconductivity] almost certainly has something to do with the novelty of the material. There's something different going on in there.”

The previous research, published on August 16, 2019 in the journal Science, described the rare and exotic ground state known as spin-triplet superconductivity in uranium ditelluride. The discovery marked the first clue that uranium ditelluride is worth a second look, due to its unusual physical properties and its high potential for use in quantum computers.

“This is indeed a remarkable material and it’s keeping us very busy,” said Johnpierre Paglione, a professor of physics at UMD, the director of UMD’s Center for Nanophysics and Advanced Materials (CNAM; soon to be renamed the Quantum Materials Center) and a co-author of the paper. “Uranium ditelluride may very well become the ‘textbook’ spin-triplet superconductor that people have been seeking for dozens of years and it likely has more surprises in store. It could be the next strontium ruthenate—another proposed spin-triplet superconductor that has been studied for more than 25 years.”

Superconductivity is a state in which electrons travel through a material with perfect efficiency. By contrast, copper—which is second only to silver in terms of its ability to conduct electrons—loses roughly 20% power over long-distance transmission lines, as the electrons bump around within the material during travel.

Lazarus superconductivity is especially strange, because strong magnetic fields usually destroy the superconducting state in the vast majority of materials. In uranium ditelluride, however, a strong magnetic field coupled with specific experimental conditions caused Lazarus superconductivity to arise not just once, but twice.

For Butch, Paglione and their team, the discovery of this rare form of superconductivity in uranium ditelluride was serendipitous; the study’s lead author, CNAM Research Associate Sheng Ran, synthesized the crystal accidentally while attempting to produce another uranium-based compound. The team decided to try some experiments anyway, even though previous research on the compound hadn’t yielded anything unusual.

The team’s curiosity was soon rewarded many times over. In the earlier Science paper, the researchers reported that uranium ditelluride’s superconductivity involved unusual electron configurations called spin triplets, in which pairs of electrons are aligned in the same direction. In the vast majority of superconductors, the orientations—called spins—of paired electrons point in opposite directions. These pairs are (somewhat counterintuitively) called singlets. Magnetic fields can more easily disrupt singlets, killing superconductivity.

Spin triplet superconductors, however, can withstand much higher magnetic fields. The team’s early findings led them to the National MagLab, where a unique combination of very high-field magnets, capable instrumentation and resident expertise allowed the researchers to push uranium ditelluride even further.

At the lab, the team tested uranium ditelluride in some of the highest magnetic fields available. By exposing the material to magnetic fields up to 65 teslas—more than 30 times the strength of a typical MRI magnet—the team attempted to find the upper limit at which the magnetic fields crushed the material’s superconductivity. Butch and his team also experimented with orienting the uranium ditelluride crystal at several different angles in relation to the direction of the magnetic field.

At about 16 teslas, the material’s superconducting state abruptly changed. While it died in most of the experiments, it persisted when the crystal was aligned at a very specific angle in relation to the magnetic field. This unusual behavior continued until about 35 teslas, at which point all superconductivity vanished and the electrons shifted their alignment, entering a new magnetic phase.

As the researchers increased the magnetic field while continuing to experiment with angles, they found that a different orientation of the crystal yielded yet another superconducting phase that persisted to at least 65 teslas, the maximum field strength the team tested. It was a record-busting performance for a superconductor and marked the first time two field-induced superconducting phases have been found in the same compound. 

Instead of killing superconductivity in uranium ditelluride, high magnetic fields appeared to stabilize it. While it is not yet clear exactly what is happening at the atomic level, Butch said the evidence points to a phenomenon fundamentally different than anything scientists have seen to date.

“I'm going to go out on a limb and say that these are probably different—quantum mechanically different—from other superconductors that we know about,” Butch said. “It is sufficiently different, I think, to expect it will take a while to figure out what's going on.”

On top of its convention-defying physics, uranium ditelluride shows every sign of being a topological superconductor, as are other spin-triplet superconductors, Butch added. Its topological properties suggest it could be a particularly accurate and robust component in the quantum computers of the future.

“The discovery of this Lazarus superconductivity at record-high fields is likely to be among the most important discoveries to emerge from this lab in its 25-year history,” said National MagLab Director Greg Boebinger. “I would not be surprised if unraveling the mysteries of uranium ditelluride leads to even stranger manifestations of superconductivity in the future.”


This release was adapted from text provided by the National High Magnetic Field Laboratory.

In addition to Butch, Paglione and Ran, UMD-affiliated co-authors of the research paper include physics postdoctoral researcher Yun Suk Eo; physics graduate students I-Lin LiuDaniel Campbell and Christopher Eckberg; undergraduate physics major Paul Neves, physics faculty assistant Wesley Fuhrman; CNAM (QMC) Assistant Research Scientist Hyunsoo Kim and CNAM (QMC) Associate Research Scientist Shanta Saha.

The research paper, “Extreme magnetic field-boosted superconductivity,” Sheng Ran, I-Lin Liu, Yun Suk Eo, Daniel Campbell, Paul Neves, Wesley Fuhrman, Shanta Saha, Christopher Eckberg, Hyunsoo Kim, Johnpierre Paglione, David Graf, Fedor Balakirev, John Singleton and Nicholas Butch, was published in the journal Nature Physics on October 7, 2019.

This work was supported by the Schmidt Science Fellows program (in partnership with the Rhodes Trust), the National Science Foundation (Award Nos. DMR-1610349, DMR-1157490, DMR-1644779), the U.S. Department of Energy (Award No. DE-SC-0019154), the Gordon and Betty Moore Foundation’s EPiQS Initiative (Award No. GBMF4419), and the State of Florida. The content of this article does not necessarily reflect the views of these organizations.

Media Relations Contact: Matthew Wright, 301-405-9267, This email address is being protected from spambots. You need JavaScript enabled to view it.

Original story: https://cmns.umd.edu/news-events/features/4500

Hans R. Griem, 1928-2019

Prof. Emeritus Hans R. Griem, a noted expert in high-temperature plasmas and spectroscopy, died on October 2, 2019.

Prof. Griem received his Ph.D. from the Universität of Kiel, Germany, in 1954 and accepted a Fulbright Fellowship working on upper atmospheric physics at UMD. He returned to Universität Kiel for a two-year appointment before joining the UMD faculty in 1957.   He was well known for his research on radiation from highly ionized atoms in high temperature plasmas, and for his work on spectral line broadening (and shifts) in dense plasmas.  He was a consultant with Los Alamos National Laboratory during most of his career, and retired from UMD in 1994.

He was a fellow of the American Physical Society and a referee for several journals, including Physical Review Letters.  Among his accolades were a Guggenheim Fellowship, a Humboldt Award and the William F. Meggers Award of the Optical Society.  In 1991 he received the James Clerk Maxwell Prize in Plasma Physics for "his numerous contributions to experimental plasma physics and spectroscopy, particularly in the area of improved diagnostic methods for high temperature plasmas, and for his books on plasma spectroscopy and spectral line broadening in plasmas that have become standard references in the field."

Prof. Griem was instrumental in founding the UMD Institute Research in Electronics and Applied Physics, and served as one of the first directors of IREAP. He advised over 40 doctoral students in his time at UMD.

Jim Griffin, Hans Griem and Doug Currie in 2001.

In The Washington Post obituary published Oct. 6, 2019, the Griem family kindly directed donations in Prof. Griem's name to UMD Physics.