International Quantum Tensor Network

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The aim of this project is to form a network of international collaboration tasked with creating a new way to write software for quantum computers based upon using tensor networks.Tensor networks are amongst the very best ways to model quantum systems on a classical computer. The possibilities for a quantum system are so numerous that they cannot all be described on any classical computer. The problem is a profound one - to go from describing 30 quantum spins (around the limit for today's supercomputers) to 31 doubles the computational requirements, so evolving conventional hardware cannot keep up with the problems that we want to solve. Tensor networks get round this by focusing on the parts of the system that really matter so that we can get an approximate - but highly accurate - description that we can systematically improve as our computer gets better. Some of the most accurate predictions in quantum mechanics have been made using this approach.Tensor networks are also an excellent way of making use of the limited quantum resources available on near term intermediate-scale quantum (NISQ) computers.These computers have limited power - measured by a property of quantum systems known as entanglement - due to the degrading effect that the environment has on quantum correlations. Tensor networks use precisely this entanglement measure to determine how to approximate the most important properties of the quantum system, and it is for this reason that they are such a good way to programme quantum computers. This approach to quantum software has just begun to be developed, but already shows excellent promise. The International Quantum Tensor Network is designed to place the UK at the centre of an international push to further develop the approach. The applications of this quantum software will help to use quantum computers to simulate other quantum systems - with the promise ultimately to revolutionise quantum problems in chemistry and drug design - but also to solve a variety of classical problems including those in machine learning.