Student Research Opens New Doors in Quantum Image Processing
October 25, 2021
When Mercy Amankwah joined the Computing Sciences Area’s Summer Program at Lawrence Berkeley National Laboratory (Berkeley Lab) in June 2021, she’d never worked in quantum computing before and found the subject intriguing, but intimidating. By the end of the summer, she had helped advance the field.
Amankwah, a third-year applied mathematics Ph.D. student at Case Western University, is originally from Sunyani, Ghana. She came to Berkeley Lab virtually for the 12-week summer program through Sustainable Research Pathways (SRP), founded in 2015 as a partnership between Berkeley Lab and the Sustainable Horizons Institute. The SRP program connects students from groups that are underrepresented in the sciences to staff scientists at the lab to facilitate research collaborations and bolster the development of young talent.
While Amankwah had studied image processing and thought it would be a suitable area for her work at the lab over the summer, she was also curious about quantum computing. Through a speed-dating-esque process that helps match students and researchers, she found a project at Berkeley Lab that would enable her to take on the challenge of image processing using some novel quantum-based methods.
Managed by Principal Investigator Talita Perciano of Berkeley Lab's Scientific Data Division and Co-P.I. Roel Van Beeumen of the Applied Math & Computational Research Division, the project uses flexible representation for quantum images (FRQI), a method of processing images using quantum computing first developed in 2011.
“Quantum image processing is a topic that has been studied in the literature for some time now,” said Perciano. “However, we were interested in focusing on developing quantum image representations that could benefit scientific data specifically, and nobody has looked into that before.”
FRQI translates each image into data corresponding to pixel locations and colors that can be translated through qubit instructions. This algorithm for quantum image compression shows great promise as a way to handle images that take large amounts of conventional computational resources to process and do so with a small number of qubits relative to other quantum computing methods.
“At the moment, image and video data constitute a greater part of available data, whether it's being transferred on the Internet or sitting in repositories,” Amankwah explained. “Processing this kind of data, especially scientific images, requires high computational power. Quantum computers offer this; however, there is the need to encode the data in a way that quantum processors will understand.”
Quantum image processing, like quantum computing itself, is in its early stages, with many methods proving themselves on images as small as two pixels by two pixels. The work being done today to refine methods and algorithms will allow image processing to scale up as quantum computers increase in power.
While quantum computing could one day bring exponential improvements over classical computers in image storage and processing speed, the current methods involved are more error-prone than classical methods because when using current quantum computers, each qubit operation, or “gate,” in an algorithm lengthens its run time and introduces failure points. Thus, a key focus of this area of research is to shorten and simplify the number of gates in image-processing methods to make them more consistent and viable, while also expanding what’s possible using FRQI.
All of this was a lot to absorb for Amankwah, who had never worked in quantum computing research before.
“I am nothing but thankful to have had such a team of scientists to work with,” she said. “This being my first experience working at a lab -- and a national lab for that matter -- was a little intimidating for me at the beginning of the summer program.”
Despite concerns about feelings of “impostor syndrome,” “I felt like I belonged,” Amankwah said. “My mentor Talita and supervisor Roel managed to challenge me every week with a new task.”
These tasks built on one another to the point that the group created publishable work. While FRQI was developed to process square images, the team showed that the method works for rectangular ones as well. In each case, the computational load is simply a function of the size of the image, with no penalty resulting from the different shape. This clears a major hurdle to the viability of quantum image processing, due to the ubiquity of rectangular images.
“Mercy's hard work led us to come up with a formal generalization of quantum image representation that embraces several of the quantum image representations proposed in the literature so far,” said Perciano, noting it “is the most efficient representation existent so far, and it can be used for N-dimensional data.”
The group also found a way to streamline a key component of FRQI that involves manipulating qubits. When FRQI was first developed, qubits would receive instructions to rotate by a specified angle to encode certain elements, such as the color of an individual pixel. Each pixel would get its own corresponding rotation instructions, adding time and complexity to the process. Amankwah, Perciano, and Van Beeumen found that the same results can be achieved by applying standardized rotation instructions across every qubit at once, producing significant gains in efficiency and consistency of the FRQI process.
“We used the uniformly controlled rotation, which handled all the rotation simultaneously, as opposed to applying controlled rotation gates to every pixel the image has,” Amankwah explained. Through these efforts, the team created a software package for the project called QPIXL (Quantum Image Pixel Library) and built versions in C++ and MATLAB.
An Impactful Journey
The team, which also includes E. Wes Bethel (Scientific Data Division) and Daan Camps (Applied Mathematics and Computational Research), is preparing an article on their findings, with Amankwah as the first author. It will be submitted to a high-impact scientific journal.
Through her experience with the CS Area’s Summer Program, Amankwah, who Perciano called “curious, analytical, humble, committed, persistent,” found that her scientific abilities and processes evolved as well.
“I have been able to identify some aspects in my journey as a researcher that I'll have to polish and some habits I'll have to cultivate to prepare me to contribute meaningful research to the scientific community and beyond,” she said. “The most valuable thing about this experience is how I saw myself grow when working with my team.”
About Berkeley Lab
Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory and its scientists have been recognized with 14 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory, managed by the University of California for the U.S. Department of Energy’s Office of Science.
DOE’s 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, please visit energy.gov/science.