Classical simulations of Gaussian boson sampling

Abstract

There is a lot of effort currently being poured into the development of quantum technologies in the hope that they will speed up certain tasks significantly. Boson sampling is a task that can be run on a near-term device and so was proposed as a promising candidate for a demonstration of quantum advantage. As experiments are performed of a variant, Gaussian boson sampling, claiming to have demonstrated this, it is increasingly important to know where that boundary lies and to improve classical algorithms for simulating Gaussian boson sampling. Furthermore, there has been a wealth of applications of Gaussian boson sampling suggested beyond just a method for reaching quantum advantage. These applications raise the question of whether a quantum-inspired classical algorithm could be used to improve current methods of solving these problems. In this thesis we explore approximate Monte Carlo and chain-rule algorithms for simulating Gaussian boson sampling to minimise the complexity of sampling under perfect conditions with the aim that they can be applied to quantum-inspired algorithms. They could also be adapted to include experimental imperfections for simulating experiments.

Date of Award	25 Jan 2022
Original language	English
Awarding Institution	University of Bristol
Supervisor	Anthony Laing (Supervisor) & Josh Nunn (Supervisor)