Next Generation Quantum Algorithms for Simulation and Machine Learning

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The main aim of my proposal is to determine the minimal resources needed to provide a practical quantum advantage for quantum computers for simulation of chemistry and machine learning. I aim to achieve this by constructing a unified framework that allows stochastic simulation methods to be combined with other state of the art deterministic methods to better approximate quantum dynamics. Further, I will develop rigorous methods for simulating quantum electrodynamics in quantum systems that bounds the number of times a subroutine that provides the matrix elements of the Hamiltonian (the matrix that yields the energy of a system) must be called to perform the simulation. I will further develop a new model for deep learning for quantum systems that tolerates entanglement and avoids gradient decay by using quantum generative pre-training. This work will be supported by a minimum of two graduate students who will be recruited and trained at the University of Toronto and will involve close collaboration with researchers within industry (Google) as well as academia (University of Washington and the University of Technology Sydney).