To Tune Up Your Quantum Computer, Better Call an AI Mechanic

A high-end race car engine needs all its components tuned and working together precisely to deliver top-quality performance. The same can be said about the processor inside a quantum computer, whose delicate bits must be adjusted in just the right way before it can perform a calculation. Who’s the right mechanic for this quantum tuneup job? According to a team that includes scientists at JQI and the National Institute of Standards and Technology (NIST), it’s an artificial intelligence, that’s who.

The team’s paper in the journal Physical Review Applied outlines a way to teach an AI to make an interconnected set of adjustments to tiny quantum dots, which are among the many promising devices for creating the quantum bits, or “qubits,” that would form the switches in a quantum computer’s processor.quantumdots physicspaper explorations v7 01This artist's conception shows how the research team used artificial intelligence (AI) and other computational techniques to tune a quantum dot device for use as a qubit. The dot's electrons are corralled by electrical gates, whose adjustable voltages raise and lower the "peaks" and "valleys" in the large circles. As the gates push the electrons around, sensitive measurement of the moving electrons creates telltale lines in the black and white images, which the AI uses to judge the state of the dot and then make successive adjustments to the gate voltages. Eventually the AI converts a single dot (leftmost large circle) to a double dot (rightmost), a process that takes tedious hours for a human operator. (Credit: B. Hayes/NIST)

Precisely tweaking the dots is crucial for transforming them into properly functioning qubits, and until now the job had to be done painstakingly by human operators, requiring hours of work to create even a small handful of qubits for a single calculation. 

A practical quantum computer with many interacting qubits would require far more dots — and adjustments — than a human could manage, so the team’s accomplishment might bring quantum dot-based processing closer from the realm of theory to engineered reality.

“Quantum computer theorists imagine what they could do with hundreds or thousands of qubits, but the elephant in the room is that we can actually make only a handful of them work at a time,” said Justyna Zwolak, a NIST mathematician. “Now we have a path forward to making this real.”

A quantum dot typically contains electrons that are confined to a tight boxlike space in a semiconductor material. Forming the box’s walls are several metallic electrodes (so-called gates) above the semiconductor surface that have electric voltage applied to them, influencing the quantum dot’s position and number of electrons. Depending on their position relative to the dot, the gates control the electrons in different ways.

To make the dots do what you want — act as one sort of qubit logic switch or another, for example — the gate voltages must be tuned to just the right values. This tuning is done manually, by measuring currents flowing through the quantum dot system, then changing the gate voltages a bit, then checking the current again. And the more dots (and gates) you involve, the harder it is to tune them all simultaneously so that you get qubits that work together properly.

In short, this isn’t a gig that any human mechanic would feel bad about losing to a machine. 

“It’s usually a job done by a graduate student,” said graduate student Tom McJunkin of the University of Wisconsin-Madison’s physics department and a co-author on the paper. “I could tune one dot in a few hours, and two might take a day of twiddling knobs. I could do four, but not if I need to go home and sleep. As this field grows, we can’t spend weeks getting the system ready — we need to take the human out of the picture.”

Pictures, though, are just what McJunkin was used to looking at while tuning the dots: The data he worked with came in the form of visual images, which the team realized that AI is good at recognizing. AI algorithms called convolutional neural networks have become the go-to technique for automated image classification, as long as they are exposed to lots of examples of what they need to recognize. So the team’s Sandesh Kalantre, under supervision from Jake Taylor, a Fellow of JQI and the Joint Center for Quantum Information and Computer Science (QuICS), created a simulator that would generate thousands of images of quantum dot measurements they could feed to the AI as a training exercise.

"The simulator allows us to create a large dataset of artificial devices, which can model the real devices one might encounter in the lab," said Kalantre, a Lanczos Graduate Fellow at QuICS.

The team started small, using a setup of two quantum dots, and they verified that within certain constraints their trained AI could auto-tune the system to the setup they desired. It wasn’t perfect — they identified several areas they need to work on to improve the approach’s reliability — and they can’t use it to tune thousands of interconnected quantum dots as yet. But even at this early stage its practical power is undeniable, allowing a skilled researcher to spend valuable time elsewhere.

"This concept — using physical modeling to improve automated systems with machine learning — opens up new vistas for a wide range of experimental systems," said Taylor. "And not just in physics."

This story was originally published by NIST News. It has been adapted with minor changes here.

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Physics Students Receive NSF Graduate Research Fellowships

Five current students and a recent alumnus of the Department of Physics received prestigious National Science Foundation (NSF) Graduate Research Fellowships, which recognize outstanding graduate students in science, technology, engineering, and mathematics.

NSF Graduate Research Fellowship Program logo

“I’m so happy to see our students honored with prestigious NSF Graduate Research Fellowships that acknowledge their hard work in their research endeavors and in the classroom,” said  Amitabh Varshney, dean of the College of Computer, Mathematical, and Natural Sciences (CMNS).

Across the university, 29 undergraduates and recent alumni were among the fellowship winners announced by the NSF. As a result, UMD ranks ninth in the nation and second in the Big Ten for the number of fellows who received their bachelor’s degrees at the university.

The college’s 17 awardees include eight current undergraduates with CMNS majors, three recent alumni who received bachelor’s degrees in CMNS majors, and six current graduate students enrolled in CMNS programs (one of whom is also a recent graduate).

Undergraduate student fellowship recipients:

Alumni fellowship recipients:

NSF fellows receive three years of support, including a $34,000 annual stipend, a $12,000 cost-of-education allowance to the graduate degree-granting institution, international research and professional development opportunities, and access to a supercomputer.

The NSF Graduate Research Fellowship Program helps ensure the vitality of the human resource base of science and engineering in the United States and reinforces its diversity. The program recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based master’s and doctoral degrees at accredited U.S. institutions.

Since 1952, NSF has funded more than 50,000 Graduate Research Fellowships out of more than 500,000 applicants. Currently, 42 fellows have gone on to become Nobel laureates, and more than 450 have become members of the National Academy of Sciences.


Read about the other CMNS recipients here.
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HAWC’s Measurement of the Highest Energy Photons Sets Limits on Violations of Relativity

New measurements confirm, to the highest energies yet explored, that the laws of physics hold no matter where you are or how fast you're moving. Observations of record-breaking gamma rays prove the robustness of Lorentz Invariance—a piece of Einstein's theory of relativity that predicts the speed of light is constant everywhere in the universe. The High Altitude Water Cherenkov observatory in Puebla, Mexico detected the gamma rays coming from distant galactic sources. UMD authors on the paper were Jordan Goodman, Andy Smith, Bob Ellsworth, Kristi Engel, Israel Martinez-Castellanos, Michael Schneider and Elijah Tabachnick.

Goodman Hawc 2020This compound graphic shows a view of the sky in ultra-high energy gamma rays. The arrows indicate the four sources of gamma rays with energies over 100 TeV from within our galaxy (courtesy of the HAWC collaboration) imposed over a photo of the HAWC Observatory’s 300 large water tanks. The tanks contain sensitive light detectors that measure showers of particles produced by the gamma rays striking the atmosphere more than 10 miles overhead. Credit: Jordan Goodman

"How relativity behaves at very high energies has real consequences for the world around us," said Pat Harding, an astrophysicist in the Neutron Science and Technology group at Los Alamos National Laboratory and a member of the HAWC scientific collaboration. "Most quantum gravity models say the behavior of relativity will break down at very high energies. Our observation of such high-energy photons at all raises the energy scale where relativity holds by more than a factor of a hundred."

Lorentz Invariance is a key part of the Standard Model of physics. However, a number of theories about physics beyond the Standard Model suggest that Lorentz Invariance may not hold at the highest energies. If Lorentz Invariance is violated, a number of exotic phenomena become possibilities. For example, gamma rays might travel faster or slower than the conventional speed of light. If faster, those high-energy photons would decay into lower-energy particles and thus never reach Earth.

The HAWC Gamma Ray Observatory has recently detected a number of astrophysical sources which produce photons above 100 TeV (a trillion times the energy of visible light), much higher energy than is available from any earthly accelerator. Because HAWC sees these gamma rays, it extends the range that Lorentz Invariance holds by a factor of 100 times.

"Detections of even higher-energy gamma rays from astronomical distances will allow more stringent the checks on relativity. As HAWC continues to take more data in the coming years and incorporate Los Alamos-led improvements to the detector and analysis techniques at the highest energies, we will be able to study this physics even further," said Harding.

Story courtesy of Los Alamos National Laboratory. Article:

Physics Student Named 2020 Goldwater Scholar

moroch goldwaterScott Moroch, courtesy of same

Scott Moroch was one of four CMNS undergraduates to receive a scholarship from the Barry Goldwater Scholarship and Excellence in Education Foundation, which encourages students to pursue advanced study and research careers in the sciences, engineering and mathematics.  Over the last decade, UMD’s nominations yielded 33 scholarships—the most in the nation, followed by Stanford University with 32. Harvard University, the Massachusetts Institute of Technology and Johns Hopkins University also rank in the top 10. Moroch is the eighth physics undergraduate recipient in the past four years. 

“Our scholars are a uniquely talented group, already making discoveries in their fields of study—from developing more stable batteries and innovative power supplies to streamlining the pathway of drug design and understanding the contributions of RNA in cancer and other diseases,” said Robert Infantino, associate dean of undergraduate education in the College of Computer, Mathematical, and Natural Sciences. Infantino has led UMD’s Goldwater Scholarship nominating process since 2001.

Moroch was among the 396 Barry Goldwater Scholars selected from 1,343 students nominated nationally this year. Goldwater Scholars receive one- or two-year scholarships that cover the cost of tuition, fees, books, and room and board up to $7,500 per year. These scholarships are a stepping-stone to future support for the students’ research careers. The Goldwater Foundation has honored 70 UMD winners and five honorable mentions since the program’s first award was given in 1989.

Moroch, a native of Wayne, New Jersey, designed his own particle accelerator when he was still in high school. Since enrolling at Maryland, he has been working on UMD’s cyclotron with Timothy Koeth, an assistant professor in the Department of Materials Science and Engineering and the Institute for Research in Electronics and Applied Physics.

A cyclotron is a type of particle accelerator that won its inventor the Nobel Prize in physics in 1939. The beams that cyclotrons produce, while potentially dangerous, accomplish wondrous things—killing cancer cells with extreme precision, for instance, or changing atoms into a different element altogether.

Moroch is working with Koeth to develop a novel cyclotron storage ring for Lockheed Martin. The company is interested in using the technology for a new class of power supplies for aerospace electric propulsion systems that can carry things into the solar system and beyond.

With initial funding from Lockheed, Moroch showed that a cyclotron design could be effective, but it was unstable. So the company decided to fund a more ambitious project at UMD—where the instabilities could be factored out. Moroch now leads a significant portion of the research team.

“Scott is no ordinary exceptional student,” said E. H. “Ned” Allen, senior fellow and chief scientist at Lockheed Martin. “He has won so much respect that he has become a colleague and a first-line team member—even though still an undergraduate.”

Last summer and fall, Moroch led a team of three undergraduates in assembling and upgrading a low-energy storage ring as part of the project. A storage ring is a type of particle accelerator in which a continuous or pulsed particle beam may be kept circulating typically for many hours. The students retrieved used components from another university, restored the retrieved components, designed and fabricated missing subsystems, reassembled them into a working ion storage ring, and brought the whole assembly under high vacuum. The new accelerator got up and running early in the spring semester, achieving what’s known as “first beam.”

“In the past 20 years, I have mentored several dozen undergraduate researchers, and Scott Moroch is the first that has demonstrated the entire cycle of research and brought in substantial research funds,” Koeth said.

Moroch also helped design an electron beamline in collaboration with Los Alamos National Laboratory. In graduate school, Moroch plans to pursue a Ph.D. in accelerator physics.

Read about the other CMNS recipients here.