QLearning

Both machine learning algorithms and quantum computing are revolutionising information processing. While so-called "supervised learning" and "unsupervised learning" usually aim to find structures in large amounts of data, "reinforcement learning" is more concerned with finding a constructive solution to a given problem. However, problem solving using reinforcement learning is usually quite time-consuming.

Quantum algorithms accelerate this learning process - we have demonstrated the functional principle for this theoretically and experimentally in recent publications. However, many challenges still need to be overcome before accelerated learning with quantum algorithms can be used to solve real-world problems, for example in the field of navigation.

The challenge here is that for reinforcement learning with quantum algorithms we need low-error quantum processors and good quantum algorithms. However, the available quantum processors are still severely limited in the number of quantum gates that can be executed in succession until the resulting errors get out of hand. Optimised implementation strategies for quantum algorithms are therefore needed to get one step closer to real applications.