Mathematics for quantum computation and many-body theory (QOMBINE)
A cross-disciplinary project between mathematics, chemistry, and physics
Qombine group members as of October 2023 (Makoto Yamashita not on photo). (Photo: Elisabeth Seland, Department of Mathematics, UiO.)
About the project
Modern computers and their ever improving computing power have defined the technological advancements of our times. Their performance will eventually reach its limits and a new computational paradigm is currently being developed to enable future advances: Quantum computing aims to exploit the features of quantum mechanics in order to solve computational tasks faster than it would be possible on classical computers.
Developing this technology and understanding its capabilities is a massive and quickly evolving endeavor. A collective effort of the scientific community drawing on expertise across various disciplines is key to deliver sound and long lasting progress. Our team of mathematicians, physicists and quantum chemists will concentrate on theoretical questions central to noise-resistant quantum computation and its application to many-body theory and information processing. Answering these questions requires novel tools anchored across our different disciplines. QOMBINE brings together a unique team of experts in Norway to develop these tools. This will lay the foundations for a new and rich cross-fertilization of research in Mathematics, Physics and Chemistry in Norway.
Financing
This project is funded by the Reseach Council of Norway . Funding ID: 324944. Total budget approx. 25 million NOK.
More group photos
Qombine+ (group members and close collaborators), October 2023. (Photo: Elisabeth Seland, Department of Mathematics, UiO.)Group members as of September 2022. (Photo: Department of Mathematics, UiO.)
Fuchs, Franz Georg
(2024).
Introduction to Tensor Networks.
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Tensor networks (TN) have important applications in mathematics, chemistry, physics, and machine learning. They can for instance be used to describe quantum many-body systems, simulate quantum circuits, or compress neural networks. In this talk I will give a gentle introduction to the mathematical framework of TNs. After introducing the basic concepts and notation, I well go through some well-known algorithms/methods for sampling, calculating inner products, and time evolution.
Fuchs, Franz Georg
(2023).
Hamiltonians with time evolution restricted to subspaces.
Fuchs, Franz Georg
(2022).
An introduction to quantum error mitigation.
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Quantum error mitigation (QEM) aims to reduce the errors that occur on quantum chips when a quantum algorithm is executed. In this talk I will give an introduction to two popular methods for QEM. The first one is based on extrapolation techniques, and the second on quasiprobabilistic decompositions. I will also highlight differences and similarities of QEM and quantum error correction.
Müller-Hermes, Alexander
(2022).
Annihilating Entanglement Between Cones.
Müller-Hermes, Alexander
(2022).
Fault-tolerant Coding for Quantum Communication.
Müller-Hermes, Alexander
(2022).
Capacities of quantum channels.
