Sibylle Sampson, 1929-2021

Sibylle Sampson, a crucial member of the Department of Physics during a period of remarkable growth, died August 8 at the Ginger Cove retirement community in Annapolis.

SampsonJohn Toll praises Sibylle Sampson at her retirement party in 1991.John Toll praises Sibylle Sampson at her retirement party in 1991. joined the department in 1960 as a stenographer, and rose to become Director of Finance and the utterly essential aide to John Toll during his frenzied and fruitful expansion of UMD Physics. She was a renowned administrator and advocate of the department.

Toll’s arrival transformed the department (and by extension, elevated the research stature of the entire university). By all accounts, Sampson was immensely important, dedicated and effective in implementing Toll's plans.

Three decades ago, Sampson established the Sibylle Sampson Award  to highlight particularly innovative efforts of  physics staff members.

A native of Schwäbisch Gmünd, Germany, Sampson traveled to several countries for various jobs, and first arrived at College Park while visiting her brother, who was a postdoctoral researcher here. She eventually married her brother's roommate, an economics student, in 1959. Bob Sampson died just five months before Sibylle.

In retirement, both Sampsons enjoyed travel and boating. Sibylle was a poet in both English and German, and in 2018 published aSibylle Sampson recalls John Toll at his 2011 memorial.Sibylle Sampson recalls John Toll at his 2011 memorial. volume entitled, "Wanderings".   

Solstice

I woke into black silence,
The earth so still as if it held its
Spinning, had drowned
The breath of wind.

It is the night of darkest winter
Sol sistere – when the sun stands still,
When ancients built fires to
Chase the ghosts of night.

From the East,
Dawn slowly spreads her mantle,
Gray and reluctant first – her cloak will
Soon reveal a golden rim:

The promise of the sun
Of light embracing the earth,
Of snow vibrating in the spectrum of color -
The hope of every window in this world.

Sistere,
Stand still, o moment,
Remain.

 

New Approach to Information Transfer Reaches Quantum Speed Limit

Even though quantum computers are a young technology and aren’t yet ready for routine practical use, researchers have already been investigating the theoretical constraints that will bound quantum technologies. One of the things researchers have discovered is that there are limits to how quickly quantum information can race across any quantum device.

These speed limits are called Lieb-Robinson bounds, and, for several years, some of the bounds have taunted researchers: For certain tasks, there was a gap between the best speeds allowed by theory and the speeds possible with the best algorithms anyone had designed. It’s as though no car manufacturer could figure out how to make a model that reached the local highway limit.

But unlike speed limits on roadways, information speed limits can’t be ignored when you’re in a hurry—they are the inevitable results of the fundamental laws of physics. For any quantum task, there is a limit to how quickly interactions can make their influence felt (and thus transfer information) a certain distance away. The underlying rules define the best performance that is possible. In this way, information speed limits are more like the max score on an old school arcade game(link is external) than traffic laws, and achieving the ultimate score is an alluring prize for scientists.In a new quantum protocol, groups of quantum entangled qubits (red dots) recruit more qubits (blue dots) at each step to help rapidly move information from one spot to another. Since more qubits are involved at each step, the protocol creates a snowball effect that achieves the maximum information transfer speed allowed by theory. (Credit: Minh Tran/JQI)In a new quantum protocol, groups of quantum entangled qubits (red dots) recruit more qubits (blue dots) at each step to help rapidly move information from one spot to another. Since more qubits are involved at each step, the protocol creates a snowball effect that achieves the maximum information transfer speed allowed by theory. (Credit: Minh Tran/JQI)

Now a team of researchers, led by Adjunct Associate Professor Alexey Gorshkov, has found a quantum protocol that reaches the theoretical speed limits for certain quantum tasks. Their result provides new insight into designing optimal quantum algorithms and proves that there hasn’t been a lower, undiscovered limit thwarting attempts to make better designs. Gorshkov, who is also a Fellow of the Joint Quantum Institute, the Joint Center for Quantum Information and Computer Science (QuICS) and a physicist at the National Institute of Standards and Technology(link is external), and his colleagues presented their new protocol in a recent article published in the journal Physical Review X(link is external).

“This gap between maximum speeds and achievable speeds had been bugging us, because we didn't know whether it was the bound that was loose, or if we weren't smart enough to improve the protocol,” says Minh Tran, a JQI and QuICS graduate student who was the lead author on the article. “We actually weren't expecting this proposal to be this powerful. And we were trying a lot to improve the bound—turns out that wasn't possible. So, we’re excited about this result.”

Unsurprisingly, the theoretical speed limit for sending information in a quantum device (such as a quantum computer) depends on the device’s underlying structure. The new protocol is designed for quantum devices where the basic building blocks—qubits—influence each other even when they aren’t right next to each other. In particular, the team designed the protocol for qubits that have interactions that weaken as the distance between them grows. The new protocol works for a range of interactions that don’t weaken too rapidly, which covers the interactions in many practical building blocks of quantum technologies, including nitrogen-vacancy centers, Rydberg atoms, polar molecules and trapped ions.

Crucially, the protocol can transfer information contained in an unknown quantum state to a distant qubit, an essential feature for achieving many of the advantages promised by quantum computers. This limits the way information can be transferred and rules out some direct approaches, like just creating a copy of the information at the new location. (That requires knowing the quantum state you are transferring.)

In the new protocol, data stored on one qubit is shared with its neighbors, using a phenomenon called quantum entanglement. Then, since all those qubits help carry the information, they work together to spread it to other sets of qubits. Because more qubits are involved, they transfer the information even more quickly.

This process can be repeated to keep generating larger blocks of qubits that pass the information faster and faster. So instead of the straightforward method of qubits passing information one by one like a basketball team passing the ball down the court, the qubits are more like snowflakes that combine into a larger and more rapidly rolling snowball at each step. And the bigger the snowball, the more flakes stick with each revolution.

But that’s maybe where the similarities to snowballs end. Unlike a real snowball, the quantum collection can also unroll itself. The information is left on the distant qubit when the process runs in reverse, returning all the other qubits to their original states.

When the researchers analyzed the process, they found that the snowballing qubits speed along the information at the theoretical limits allowed by physics. Since the protocol reaches the previously proven limit, no future protocol should be able to surpass it.

“The new aspect is the way we entangle two blocks of qubits,” Tran says. “Previously, there was a protocol that entangled information into one block and then tried to merge the qubits from the second block into it one by one. But now because we also entangle the qubits in the second block before merging it into the first block, the enhancement will be greater.”

The protocol is the result of the team exploring the possibility of simultaneously moving information stored on multiple qubits. They realized that using blocks of qubits to move information would enhance a protocol’s speed.

“On the practical side, the protocol allows us to not only propagate information, but also entangle particles faster,” Tran says. “And we know that using entangled particles you can do a lot of interesting things like measuring and sensing with a higher accuracy. And moving information fast also means that you can process information faster. There's a lot of other bottlenecks in building quantum computers, but at least on the fundamental limits side, we know what's possible and what's not.”

In addition to the theoretical insights and possible technological applications, the team’s mathematical results also reveal new information about how large a quantum computation needs to be in order to simulate particles with interactions like those of the qubits in the new protocol. The researchers are hoping to explore the limits of other kinds of interactions and to explore additional aspects of the protocol such as how robust it is against noise disrupting the process.

Original story by Bailey Bedford: https://jqi.umd.edu/news/new-approach-information-transfer-reaches-quantum-speed-limit

In addition to Gorshkov and Tran, co-authors of the research paper include JQI and QuICS graduate student Abhinav Deshpande, JQI and QuICS graduate student Andrew Y. Guo, and University of Colorado Boulder Professor of Physics Andrew Lucas.

Paul Cited for Outstanding Thesis

The Division of Plasma Physics of the American Physical Society has selected Elizabeth Paul (Ph.D. '20) for the Marshall N. Rosenbluth Outstanding Doctoral Thesis Award. This prize is awarded to one person each year for the best Ph.D. thesis in plasma physics. Dr. Paul will receive a $2,000 prize and the opportunity to discuss her dissertation, “Adjoint methods for stellarator shape optimization and sensitivity analysis”, at the division's annual meeting November 8-12, 2021.

At UMD, Paul worked with Matt Landreman and Williiam Dorland studying stellarators, devices in which plasmas are confined using magnetic fields with carefully designed shaping. In her thesis, Elizabeth devised efficient methods to compute how physics properties of the plasma change if there are changes toElizabeth PaulElizabeth Paul the plasma’s shape or to the shape of the confining electromagnets. She was named a UMD Grad School Outstanding Research Assistant and received a $15,000 award from the Metro Washington Chapter of the Achievement Rewards for College Scientists (ARCS) Foundation. She twice received the IREAP Graduate Student Seminar Best Speaker Award.

Following her doctorate, Paul accepted a Presidential Postdoctoral Research Fellowship at Princeton University, returning to the campus where she graduated magna cum laude in astrophysical sciences in 2015.

Three other UMD graduates—all advised by Prof. Howard Milchberg—have received the Rosenbluth Award: Yu-Hsin Chen, Ki-Yong Kim and Thomas R. Clark, Jr.

 

 

 

Neuromorphics for Network Discovery

From neurons connected by axons to Facebook profiles connected by friendships, interaction networks lie all around us. In new work recently published in Physical Review X, Amitava BanerjeeJoseph D. HartRajarshi Roy and Edward Ott  applied machine learning tools to formulate and test a new approach to working out such interaction networks solely from the data of their observed behavior over time.

To do so, the researchers trained an artificial neural network to mimic the observed time evolution of the unknown system. They then tracked the spread of disturbances in that trained neural nSchematics of the RC trained for predicting the time series k time steps ahead. Lower: the four time series represent scalar components of X[t].Schematics of the RC trained for predicting the time series k time steps ahead. Lower: the four time series represent scalar components of X[t].etwork and used that information to infer the network structure of the original system. The method is particularly suited for the common but hard-to-solve situations—where the network dynamics are noisy, and the cause-and-effect interactions are time-lagged. The team also tested this technique on experimental and computer-simulated data from opto-electronic networks—an excellent testbed for complex dynamics—and showed that the technique is extremely effective. Determining the underlying interaction network is a key step towards understanding, predicting, and controlling the behavior of many complex dynamical systems. As such, this method offers the promise of widespread future impact for the study of networks and dynamics.

To read more, see the paper  "Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests", in Phys. Rev. X 11, 031014