Quantum-enhanced machine learning with semiconductor quantum dots

Analogue quantum simulation is an emerging field for quantum chemistry, materials design, and for solving optimisation problems. The use of analogue rather than digital quantum systems allows for the direct mapping of high-valued computational problems onto near-term hardware that may offer an advantage over classical methods. Semiconductor quantum dots have recently been shown to exhibit the controllability and precision required to for analogue quantum simulation of complex physical systems such as magnetism and topological phases in condensed matter. At Silicon Quantum Computing (SQC), we are building a scalable platform for both digital and analogue quantum computation. The digital architecture relies on the spins of single electrons and phosphorus nuclei embedded in a silicon crystal. However, the analogue approach focuses on the use of quantum dots formed using the same phosphorus donors in silicon to directly simulate “atoms” in one- and two-dimensional lattices. Using these quantum processors, we can embed computationally difficult problems to be used as a feature generator for machine learning.

This project will focus on the design, theoretical understanding and measurement of analogue quantum feature generators for quantum-enhanced machine learning. The analogue processors will be fabricated with sub-nanometre precision using a UHV scanning tunnelling microscopy hydrogen-lithography combined with phosphine dosing. The fabricated processors will then be measured in a He3 dilution refrigerator operating at a few mK using various electrical measurement techniques. The successful student will be privy to the entire fabrication and measurement process and can work with internal/external collaborators in condensed matter and quantum chemistry problems. Depending on the processor different analysis/control techniques may be developed including machine learning, feedback controls, and radio-frequency measurement techniques.

This position reports to Dr Sam Gorman and works alongside the full stack team (staff and students) funded by SQC.

Scholarship

This scholarship includes the following: