I’m a computational quantum physicist and software developer, working to build accessible, open-source quantum software at Xanadu. This includes:
PennyLane, a Python library for differentiable programming of quantum computers, and making TensorFlow and PyTorch “quantum aware”.
Strawberry Fields, a library for simulating, optimizing, and executing quantum photonic circuits on Xanadu’s hardware.
The Walrus, a C++ library for the fast calculation of intractable graph quantities required for simulating Gaussian boson sampling.
A majority of my work is in Python, but I have also dabbled in Fortran, C, and C++. I believe that code documentation is as—if not more—important as the underlying software, and this philosophy has guided the development of each of the libraries listed above. I am also an advisor for the Unitary Fund, a non-profit working to create an accessible quantum technology ecosystem. As part of the advisory board I help to source and review microgrant applications.
Before joining Xanadu, I completed my PhD in quantum computation from the University of Western Australia, with a focus on graph algorithms. A large component of my PhD included numerical simulation on high performance supercomputing clusters, working mostly with Fortran and Python.
Since then, my research interests have shifted to quantum machine learning and variational quantum algorithms. In particular, exploring how we can compute analytic quantum gradients on hardware, take advantage of the quantum geometry to improve optimization, and use machine learning to design better photonic states. For more details, see my publication list.
In addition to research and programming, I also enjoy science writing and communication. Procrastination during my PhD resulted in my writing being featured in Australian Geographic and Science, and culminated in an undergraduate textbook for computational quantum mechanics.
Luckily, my role at Xanadu has allowed me to continue down the science communication hole, and since joining I have written about, illustrated, and highlighted various results from the quantum machine learning literature, including the quantum natural gradient, quantum stochastic gradient descent, frugal shot optimization, quantum backpropagation, Gaussian boson sampling and many others. I’ve also been lucky enough to give talks on quantum machine learning and quantum photonics at conferences such as FOSDEM and PyCon.