LIACS part of European consortium to boost industrial quantum computing
The Leiden Institute of Advanced Computer Science (LIACS) is a proud member of the NExt ApplicationS of Quantum Computing (NEASQC) consortium, which recently received an ERC H2020 grant to stimulate the state-of-the-art in industrial quantum computing. LIACS contributes research and development of new quantum-enabled algorithms and applications to the project.
NISQ computers
Quantum computers are immensely powerful computers. However, building these machines is challenging. After many years of experimental efforts the first quantum computers have been build and shown to be able to execute computations that are beyond the scope of the largest classical machines. These new machines called Noisy Intermediate-Scale Quantum (NISQ) computers have two limitations: their computations suffer from imperfections caused by noise and the hardware is small in size (in number of quantum bits). These limitations make NISQ computers very different from the unlimited quantum computers that have been studied for decades, presenting a new challenge for quantum computing experts.
The NEASQC project therefore focuses on identifying methods, algorithms and applications for NISQ computers. The project participants believe NISQ computers can deliver significant benefits in addressing practical problems such as drug discovery, CO2 capture, energy management, natural language processing, breast cancer detection, probabilistic risk assessment for energy infrastructures or hydrocarbon well optimisation. On top of developing new methods and algorithms, open source NISQ programming libraries for the applications will be made available to attract new industrial users.
Leader in two lines of research
Alfons Laarman, Vedran Dunjko and Thomas Bäck of LIACS and of the Leiden applied Quantum algorithms (aQa) initiative, are involved in two lines of research. As work package leaders and academic partners, the LIACS team will apply quantum computing to enhance industry-applied machine learning and optimization schemes, and to better risk analysis computations.
The first line of research is in collaboration with the French oil company Total. ‘Quantum Machine Learning methods can be tailored for some of the most difficult optimization tasks. These include finding the optimal geophysical mesh models of the earth's crust based on satellite and ground measurements,' says Dunjko. ‘The second line of research will improve quantitative risk analysis methods, which will be applied to the French power grid infrastructure together with Électricité de France (EDF). These methods can prevent hazardous consequences of rare events, such as earthquakes and other disasters,’ clarified Laarman. Advances in these problems can offer new ways to provide more reliable and environmentally sound solutions to our future energy needs.