Quantum functional programming: A new paradigm for quantum computing with implications for machine learning and materials science

Abstract:

Quantum computing has been promised to enable exponential speed-up in certain computational problems and has the potential to revolutionise the simulation of physical systems, with implications in fields ranging from machine learning to materials discovery. So far, the majority of quantum algorithms are based on constructing a circuit of quantum logic gates, which is used to transform the state of a quantum system. Yet, we know from classical computing that not only states (variables) can be transformed but also functions of those states can be transformed themselves, enabling the modular concatenation of functions of functions. In this talk, I will introduce a recent paradigm for the quantum analog of functional programming, known as higher-order quantum computation. I will present my results on how this paradigm can be used to develop protocols for improving quantum communication, as well as new types of algorithms for the simulation of physical systems, with implications in materials discovery. Finally, I will discuss future directions of this approach, including how the same functional programming techniques could be used to build a fully quantum generalisation of neural networks in machine learning. This talk is aimed at a general computer science audience, and no prior knowledge of quantum information is assumed.

Biography:

Hlér is a postdoctoral research fellow at the Perimeter Institute and the Institute for Quantum Computing in Waterloo, Ontario. He completed his PhD in Computer Science at the University of Oxford in 2022 and subsequently held the position of postdoctoral researcher at the University of Tokyo in partnership with IBM. Hlér’s research interests are broadly concerned with understanding how insights into the foundational structures of quantum theory can be applied to the design of quantum technologies, with applications ranging from quantum communication to quantum algorithms and quantum machine learning.