Quantum optimization and machine learning
Applications of optimization and machine-learning algorithms are appearing in every industry, sometimes with enormous potential. For example, they can reduce costs, accelerate processes, or reduce risk. In practice, input data for most optimization problems is not fully deterministic, but is at least partially uncertain, which makes these problems even more complex to solve.
There are good reasons to believe that quantum computers can help solve complicated optimization problems, with and without uncertainty.
Quantum heuristics exist for combinatorial optimization; there are quantum algorithms to solve systems of linear equations or semi-definite programs, and quantum computers can naturally represent random distributions in the form of superposition states.
Our group is developing, analyzing, and applying new quantum algorithms to relevant optimization problems, for example in finance and supply-chain management. In addition, we are endeavoring to find new applications where quantum computers could lead to an advantage in the future.