Research Fellows Directory
Dr Vivien Kendon
University of Leeds
Computers let us to calculate some amazing useful things, such as the right trajectory for sending a space probe to Saturn's moons, or the best shape to make an aircraft wing, or the behaviour of two atoms as they combine to form a molecule. It is extraordinary that we can use the same computer to calculate such different things. But there are limits to what we can compute: understanding these limits is what my research is about.
One reason why our everyday digital computers work so well is because we represent the physical quantities we want to calculate as binary numbers. However, the earliest computers didn't do this. Instead, they used something like the height of a column of water, or the strength of an electrical voltage to represent a number. Also promising faster calculations are computers that use single quantum particles, such as atoms or electrons, to harness quantum effects. The rules of quantum physics tell us that quantum computers can be fundamentally faster than ordinary computers.
The standard design for a quantum computer also uses binary numbers. But for one of the hardest problems, simulation of quantum systems like atoms combining to form molecules, they work more like the old analogue computers. So, we should be able to learn some useful lessons from the pre-digital days. The first lesson is that, even though they weren't very accurate, analogue computers still performed useful computations. This is good news, because tiny quantum particles are easily disturbed by noise. If they can still provide a useful quantum simulation without being perfect, then we have a good chance of making it work.
So, with the help of my PhD and project students, my research is looking into whether laser light interacting with single atoms can help to figure out how molecules combine or how superconducting materials work. Which should also help us to understand why computing works as well as it does, and thus what the ultimate limits are on what we can compute.
Interests and expertise (Subject groups)