Using machine learning to program a quantum simulator in integrated photonics

The proposed PhD project aligns with EPSRC's strategic priority area of quantum technologies. The PhD will fit within the specific area of developing quantum enhanced simulations, led by Anthony Laing, a senior lecturer and EPSRC Early Career Fellow in the School of Physics, and a leading academic of Bristol's QET Labs. The project draws on recent advances made by Dr Laing and Dr Santagati, a senior postdoctoral associate in QET Labs, in applying quantum machine learning techniques to quantum technologies. Quantum Machine Learning is a relatively new aspect of quantum science and has been developed at Bristol through Dr Santagati's close working relationship with theoreticians within Microsoft's quantum computing activity.
Patrick Yard is an ideal candidate for this PhD studentship. Patrick studied for his B.Sc. in Physics at the University of Bath, before moving to Munich to study for his M.Sc. in Applied and Engineering physics at the TUM. During his M.Sc. he completed his master's thesis at the Walther Meissner Institute, including a 12-month research project investigating propagating quantum microwaves for applications in continuous variable quantum information processing protocols. Given this background and his performance during a recent informal interview with Dr Laing, direct entry to PhD research is more appropriate for Patrick than a year in a CDT.
From his meeting with Dr Laing, it is clear that Patrick is passionate about physics, and is ambitious to make serious contributions to cutting edge research. Having surveyed different topics for his PhD and a range of institutions in the UK and Europe, Patrick has identified Dr Laing's research group at Bristol as his top choice. After discussions with Dr Laing and Dr Santagati, Patrick has chosen the topic of using machine learning techniques to train a quantum simulator as his preferred PhD thesis. The main goal of this PhD project will be the design, realisation and testing of novel integrated photonic quantum simulators for interrogating physical systems, such as molecules. Patrick will make use of novel machine learning techniques to optimise, verify and validate the results obtained by these experiments.
During the first year of his PhD, Patrick will become familiar with the grounding and current techniques in the fields of silicon nitride photonics fabrication, quantum simulation and machine learning, working within Dr Laing's group. During this period, he will learn experimental techniques relevant to the fabrication of integrated photonic devices and processing quantum information with integrated photonics, focusing on the development of low loss quantum photonic circuits in silicon nitride (SiN). As a first project, Patrick will develop a photonics implementation of a recently conceived algorithm for training quantum simulators developed in Dr. Laing's group, working with Dr Santagati on the chip designs and with Dr. Balram for their fabrication.
In his second year, Patrick will also collaborate with theoreticians (including those at Microsoft) to optimize the simulations, and improve chip designs, with an aim to publish results and present at conferences.
In his third year, and using the expertise gained at that point, Patrick will investigate the simulation of the dynamics of open quantum systems.
By the end of his PhD, Patrick will be well trained in the field of digital and analogue quantum simulation, with a deep understanding of design and implementation of quantum information processing in integrated optics. He will be ideally placed to compete for post doctoral positions in leading experimental quantum information groups around the world, or for a career in industry.