Explores the Quantum Approximate Optimization Algorithm and its application in solving optimization problems efficiently using quantum adiabatic evolution.
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
Delves into quantum computing fundamentals, including entanglement, quantum gates, and algorithms, emphasizing unitary transformations and quantum coherence.
