Quantum Computing

Quantum computing aims at solving computationally hard problems by exploiting quantum phenomena such as tunneling, quantum superpositions and entanglement. The two paradigms of quantum computing are: universal digital quantum computing and quantum simulation.

The goal of digital quantum computing is to build a universal error-corrected scalable quantum computer. The goal of quantum simulation, on the other hand, is to mimic a model Hamiltonian in a controlled experiment. The concept of adiabatic quantum computing is a hybrid of the two. It is a universal form of quantum computing, on the other hand it builds on the quantum simulation paradigm.

The aims can be summarized as “computation by simulation”. A promising application of adiabatic quantum computing in near term devices is to solve optimization problems. As optimization problems are omnipresent in academic research and industry, the impact of improving on current classical optimization algorithms is a highly desirable goal. However, quantum speed-up from current approaches, such as thermally assisted quantum annealing, is still elusive.


Our research is dedicated to theoretical quantum physics with the aim to solve computationally challenging problems efficiently in near term quantum devices. The focus is research of coherent adiabatic and non-adiabatic processes, applications in machine learning and optimization problems as well as the investigation of implementations in different next generation qubit platforms.