T-NiSQ
Tensor Networks in Simulation of Quantum matter
About the Project
In the Quantum Science era where Noisy Intermediate-Scale Quantum (NISQ) devices are accessible, quantum information tools to guide their development play a fundamental role. With the foreseen increasing complexity of available NISQ devices, their classical simulations – that drove their development until now – will soon fail to keep up. There is thus an urgent need for increasingly powerful diagnostic tools that can be applied to quantum devices even in the quantum advantage regime. We plan to systematically develop quantum-inspired algorithms to benchmark, certify and validate quantum devices. At the center of the quantum-inspired algorithms lay tensor networks (TN), one of the most powerful paradigms for simulating quantum many-body lattice systems, both in- and out-of-equilibrium. Also exploiting TN algorithm, T-NiSQ will develop benchmarking tools for high-dimensional quantum systems in the presence of noise and test them in state-of-the-art quantum simulations and computations. The results of T-NiSQ will be an essential tool to advance our understanding of dynamical and strong- correlation effects in quantum matter also beyond the NISQ era. T-NiSQ’s results will have applications ranging from condensed matter physics over high-energy physics, quantum information theory, the design of new materials, and efficient chemical reactions.
For more information visit the QuantERA Website.
Consortium
QuantERA, the collaborative initiative of Quantum Technologies (QT) in Europe, is an alliance of 39 Research Funding Organisations from 31 countries.
As an independent network, QuantERA recognises and rewards outstanding research ideas with prospective potential. Through mutual engagement it explores and develops additional joint actions.
Acting synergistically, the QuantERA Consortium strives towards a collective goal of enhancing European scientific reinforcement in the QT domain.
Funding
This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 731473 and 101017733.