Dr. Olivia Di Matteo, an assistant professor in UBC’s department of electrical and computer engineering (ECE), has been awarded a Tier 2 Canada Research Chair (CRC) in Quantum Software and Algorithms. She is one of 15 UBC researchers appointed to a new Canada Research Chair.
The Canada Research Chairs Program (CRCP) is part of Canada’s national strategy to be one of the world’s top countries in research and development. Chairholders improve our depth of knowledge and quality of life, strengthen Canada’s international competitiveness, and help train the next generation of highly skilled people through student supervision, teaching, and the coordination of other researchers’ work.
Tier 2 Chairs are tenable for five years and renewable once. These are awarded to exceptional emerging researchers acknowledged by their peers as having the potential to lead in their field. New Tier 2 chairs receive a $20,000 annual stipend for research.
Di Matteo’s work spans developing and implementing new methods for characterizing quantum systems, synthesizing quantum circuits, and applications of quantum computing in physics, as well as many contributions to PennyLane, an open source quantum software framework that other researchers use for their own work.
She speaks about her work at ECE and future research plans (full interview).
What does your current research look like?
My group works on quantum software and algorithms, so the day-to-day is a lot of programming. On the software side, one area of focus is developing tools for automating and improving quantum compilation, which is the pipeline that translates high-level algorithms into the language of quantum hardware. On the algorithms side, we are exploring the potential use of qutrits (instead of qubits) in quantum algorithms and working on some techniques for noise mitigation.
What is something people wouldn’t expect about your research topic?
That there are problems that are hard even for quantum computers. There’s a serious amount of hype around my field right now, and quantum computers are often presented as super-advanced machines that will solve every problem exponentially faster. There are definitely some specific (but important!) problems for which we expect this will be the case (once we overcome the major engineering hurdles of building them, of course). But there are classes of problems we believe will remain hard.
What are your future research plans?
Lately, I’ve been diving into some applications of quantum computing to nuclear theory and particle physics, which has been really fun, since my training is actually as a physicist. The mapping of those problems to quantum algorithms in software through the compilation and optimization process is really interesting, and I’m hopeful that with some advances on the software front, we’ll soon be able to leverage the hardware to solve more realistic problems.
I’m also thinking about how we can make quantum computing software more accessible (e.g. through better abstraction and helpful debugging tools), so that more people can use the technology in their own work.