International Quantum Tensor Network
Lead Research Organisation:
University College London
Department Name: London Centre for Nanotechnology
Abstract
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.
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.
Organisations
- University College London (Lead Research Organisation)
- Technical University of Munich (Project Partner)
- Max Planck Institutes (Project Partner)
- Ghent University (Project Partner)
- Delft University of Technology (Project Partner)
- Flatiron Institute (Project Partner)
- University of Bonn (Project Partner)
- University of Vienna (Project Partner)
- Massachusetts Institute of Technology (Project Partner)
People |
ORCID iD |
| Andrew Green (Principal Investigator) |
| Description | The prominence of the UK in the tensor network approach to quantum software has been considerably enhanced by its funding of the International Quantum Tensor Network [EP/Wo26872/1]. This Network has brought together an international community of researchers across 22 countries (on 6 continents) thus far, including 73 universities and 18 quantum companies, including IBM, Google, Quantinuum (in the US and UK), Orca Computing, Microsoft, Phasecraft and Zapata. The Network has so far held 2 plenary meetings, at UCL (London) in June 2022 and at the Center for Computational Quantum Physics (New York City) in March 2023. IQTN have also provided financial support to a workshop in Dundee in January 2023, which was organised by members of the IQTN community. IQTN has 2 further plenary meetings planned, at TUM Raitenhaslach (Germany) in July 2023 and the University of Strathclyde (Glasgow) in January 2023. IQTN will also provide financial and administrative support to further 6 planned workshops, taking place in various locations across the US, UK and mainland Europe. Workshop organisers become Network Partners. Our activities are valued by our international partners, shown by the significant financial contributions we have received from collaborating parties including approximately $100,000 from the Flatiron Institute, €32,000 from Technical University of Munich, and $3000 respectively from IBM, Google and Quantinuum. Network activities provide opportunities for new collaborations to emerge. There are 2 known grant applications coming out of IQTN activities, with others likely to follow. We also expect to see future publications crediting IQTN. |
| First Year Of Impact | 2022 |
| Impact Types | Societal |