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Meet the Intern Using Quantum Computing to Study the Early Universe
Juliette Stecenko is using modern supercomputers and quantum computing platforms to perform astronomy simulations that may help us better understand where we came from.
May 15, 2020
Juliette Stecenko, shown at the Green Bank Observatory during her time as a student at Rutgers University, worked with Michael McGuigan of Brookhaven Lab's Computational Science Initiative to develop a quantum computing approach for tackling cosmological questions by breaking them into smaller problems.
With the help of the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Juliette Stecenko is exploring cosmology—a branch of astronomy that investigates the origin and evolution of the universe, from the Big Bang to today and into the future. As an intern through DOE’s Science Undergraduate Laboratory Internships (SULI) program, administered at Brookhaven by the Office of Educational Programs (OEP), Stecenko is using modern supercomputers and quantum computing platforms to perform astronomy simulations that may help us better understand where we came from.
Stecenko works under the guidance of Michael McGuigan, a computational scientist in the quantum computing group at Brookhaven’s Computational Science Initiative. The two have been collaborating on simulating Casimir energy—a small force that two electrically neutral surfaces held a tiny distance apart will experience from quantum, atomic, or subatomic fluctuations in the vacuum of space. The vacuum energy of the universe and the Casimir pressure of this energy could be a possible explanation of the origin and evolution of the universe, as well a possible cause of its accelerated expansion.
“Casimir energy is something scientists can measure in the laboratory and is especially important for nanoscience, or in cosmology, in the very early universe when the universe was very small,” McGuigan said.
When looking at systems that are small, such as the early universe, this type of energy becomes much more important than it is at the macro-scales we are used to experiencing, he added.
“The energy is inversely proportional to size,” McGuigan said.
For the cosmological applications that Stecenko and McGuigan are studying, Casimir energy is something that is not well understood.
“It's a form of energy that is present even if there are no charges or particles in the electromagnetic field,” McGuigan said. “We found we could simulate this on a quantum computer.”
This internship gave me a different way of looking at cosmology that can expand what I know I'm able to do as a physicist.
— Juliette Stecenko
Quantum computers use computer technology based on the principles of quantum theory, which explains the nature and behavior of energy and matter at the smallest scales.
“One of the important traits Juliette found, which wasn't known before, was that when we tried to represent this energy or any of the energies on the quantum computer, it could take an enormous number of terms,” McGuigan said.
The “terms” McGuigan is referring to are the basic units of information on a quantum computer. On a classical computer, the “terms” are a sequence of bits—values of zero and one to represent two states (think of an on and off switch). Classical computers use those two states to makes sense of and decisions about the data scientists run through the program following a prearranged set of instructions.
But when you enter the world of atomic and subatomic particles and other realms at small scales, things begin to behave in unexpected ways. These particles, for example, can exist in more than one state at a time. That’s where quantum computers are used to solve complex problems that are beyond the capabilities of a classical computer. Instead of bits, which conventional computers use, a quantum computer uses quantum bits—known as qubits.
“The easiest way to understand it is this: The bits you have on a classical computer are kind of like a quarter," Stecenko said. "And when you flip a quarter, you either get heads or tails, so it's ones and zeros. But with a qubit, you can also turn that quarter. So, you have a third component of rotation. It’s not just ones and zeros, it's ones and zeros and X's and Y's, and many other things.”
This means that a computer using qubits can store an enormous amount of information. This flexibility opens opportunities for exploring questions where the traditional laws of physics no longer apply, because qubits can represent the many possible states of the particles that make up the universe.
In recent years, scientists have successfully developed quantum algorithms to help understand the building blocks of matter. But they’ve had a much tougher time doing the same for “force-carrier” particles called bosons.
McGuigan and Stecenko tried applying quantum computing techniques to simulate boson interactions in cosmological models.
“We anticipated there wouldn't be that many terms, but then when Juliette was getting in there and trying to run it, we found there’s a lot in the terms,” McGuigan said.
This is because many bosons can occupy the same state at the same time, meaning a single state can accommodate one boson, a trillion bosons, or anything in between. That makes it tough to map bosons to qubits.
Stecenko with a poster from a previous project at Brookhaven Lab on gamma ray spectroscopy, Fall 2019. Photo courtesy Juliette Stecenko
“With bosons, the question isn’t whether the qubit represents an occupied or unoccupied state, but rather, how many qubits are needed to represent the boson state,” McGuigan said.
The large number of terms led Stecenko to make a discovery. Instead of running an algorithm for a system of 12 bosons, their algorithms could be broken up into smaller problems: two algorithms for six bosons that could then be added together.
“It’s kind of like divide and conquer,” McGuigan said. “We can divide the problem and run the calculation a few times to get a complete answer. This is a new strategy that nobody's really tried—parallel quantum computing.”
This computing approach may change how scientists tackle other simulations that involve a large number of terms.
“Cosmology is something that has always interested people since the beginning of time— looking up and finding out what's out there—but studying these questions can have broader impact,” McGuigan said. “If you build a very, very fast computer to try to understand cosmology, that very fast computing technique can be used, for example, to sift through the 3-D structures of proteins to discover new therapeutic drugs.”
From the SULI program to beyond
Stecenko graduated from Rutgers University in 2019, where she double majored in astrophysics and physics.
“My focus in undergrad was mainly astrophysics,” Stecenko said. “I did research with Eric Gawiser, Professor in the Department of Physics & Astronomy at Rutgers, and I worked on studying the clustering of Lyman-alpha emitting galaxies.”
That got her hooked on studying cosmology.
“In the fall I'm going to attend the University of Connecticut for the Ph.D. program in physics,” she said.
“In the past, my experience with cosmological research has mostly been experimental—looking at datasets and using those datasets as opposed to creating simulations, and it was based on my own previous knowledge. This internship gave me a different way of looking at cosmology that can expand what I know I'm able to do as a physicist—in my graduate education, and after that as well.”
Brookhaven National Laboratory is supported by the U.S. Department of Energy’s Office of Science. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science.
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2020-17192 | INT/EXT | Newsroom