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Quantum Tensor Engine 


Project description

Framework for rapid and easy benchmarking of quantum and classical algorithms

Progress towards large-scale quantum computing is accelerating, with technologies moving from a few qubits to up to a few hundred. However, performance of these ‘noisy intermediate-scale quantum (NISQ) computers’ is limited by quantum noise from interactions with the environment that introduces error. Highly efficient and specialised quantum algorithms could enable a software solution to the ‘hardware’ problem. The ERC-funded QTEngine project aims to support development of such algorithms with a pioneering software package based on quantum tensor networks. The ‘quantum tensor engine’ will enable fast and easy implementation of quantum simulation, quantum machine learning and optimisation algorithms to benchmark the algorithms without detailed knowledge of the underlying physics.

Objective

Quantum computers harness fundamental aspects of quantum behavior to drive exponential increases in the speed with which certain computations can be performed. They have potentially a tremendous long-term impact in areas such as quantum-many body physics and material science, and further afield in machine learning. The quantum many-body problems studied by condensed matter physicists are perhaps the most likely to yield early demonstrations of this potential. However, current and near-term intermediate-scale quantum (NISQ) devices are limited in the number of operations that they can carry out before their performance is degraded by interactions with the environment. To take advantage of these platforms and to outperform classical computers, highly efficient and specialized quantum algorithms are required. The implementation and benchmarking of these basic algorithms on different quantum computing platforms is challenging and requires a detailed knowledge of the underlying physics. Our approach is to produce a ready-to-use, highly innovative software package based upon quantum tensor networks. The Quantum Tensor Engine (QTEngine) will provide a unifying framework for both quantum and classical algorithms. The QTEngine will serve as an engine to drive fast and easy implementation of quantum simulation, quantum machine learning, and optimization algorithms. The anticipated user base include academic groups as well as commercial research and development groups.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

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(opens in new window) ERC-2023-POC

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Host institution

TECHNISCHE UNIVERSITAET MUENCHEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 150 000,00
Address
Arcisstrasse 21
80333 Muenchen
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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Beneficiaries (1)

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