Viktor Svensson

Quantum dot networks as a platform for quantum neuromorphic computing

This project sits at the intersection between machine learning and quantum computing. Both these fields have the potential to change how we perform computation, but they also face significant challenges: energy use and training of large machine learning models, and achieving fine-grained control and error mitigation in quantum systems.

In this project, we will study how one can use the natural complex dynamics of quantum systems, combined with ideas from machine learning, to accomplish tasks such as forecasting of time series or the characterization of unknown quantum states. The quantum system will be used as a reservoir computer: the input signal is processed by the reservoir and a large number of measurements are collected and analyzed by traditional machine learning methods.

This requires less fine-grained control of the quantum system, which makes it suitable for devices available in the near future. This includes small and noisy quantum computers, but also systems which are not specifically designed for computation, such as a network of quantum dots. The hope is that one can sidestep the challenges listed above, while still offering a quantum advantage in processing power.