About Jack S. Baker
Jack S. Baker is a specialist in quantum and classical computational physics, condensed matter theory, and machine learning. He earned his Master’s in Physics & Nanotechnology from the University of Leicester and went on to complete his Ph.D. in Computational Physics at University College London & the London Centre for Nanotechnology under the supervision of Prof. David R. Bowler. He is presently a Quantum Scientist at Xanadu. His previous employments include: McLaren Formula 1 where he worked on machine learning applied to biotechnologies, Diamond Light Source where he worked on nanoscale measurement techniques and Agnostiq where he developed machine learning and optimization algorithms suitable for noisy intermediate-scale quantum computers. His present research involves the development of early fault tolerant quantum algorithms for quantum chemistry and large-scale quantum mechanical simulations of condensed matter systems with a focus on ferroelectric, antiferroelectric and battery materials.
Find out more about Jack’s research interests on the dedicated page.
Open Source Projects
Jack contributes to and maintains several open source software projects including:
Pennylane: A software framework for differentiable quantum programming, similar to TensorFlow and PyTorch for classical computation. It facilitates the training of variational quantum circuits and is particularly useful for quantum machine learning research. [Github]. See also the quantum machine learning tutorials and demos [Github].
GradDFT: A JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques. The library provides significant functionality, including (but not limited to) training neural functionals with fully differentiable and just-in-time compilable self-consistent-field loops, direct optimization of the Kohn-Sham orbitals, and implementation of many of the known constraints of the exact functional. [Github].
Covalent: A Pythonic workflow tool for computational scientists, artificial intelligence/machine learning software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, high performance computing clusters, graphical processing units and cloud services. [Github].
CONQUEST: Concurrent O(N) QUantum Electronic STructure. A large-scale density functional theory code capable of simulating millions of atoms spread accross millions of physical cores on large supercomputers. [Github].
ASE: Atomic Simulation Environment. A set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations. [Github] [Gitlab].