SOURCE-LINKED INTELLIGENCE

Quantitative Quantum Matter Analysis

CORDIS · observation · Publication date unknown

we will develop and use neural networks to simulate interacting quantum many-body systems. The number of simulations for different candidate Hamiltonians will be minimized through a highly efficient, machine learning based exploration of the vast parameter space. Our advanced numerical methods, in combination with quantum simulation experiments, will also enable the exploration of the phase diagrams of the determined effective Hamiltonians, employing novel observables to reveal the underlying physics. We will focus on three particularly interesting, related material classes with the ultimate goal to distill the essence of unconventional superconductivity: cuprates, infinite layer nickelates, and bilayer nickelates. The Hamiltonian reconstruction framework established in QuaQuaMA will be applicable far beyond these materials, for example to study effective Hamiltonians in the context of light-induced superconductivity, thus generating an enormous impact in the fields of strongly correlated electron systems and quantum simulat quantum simulation, cold atoms, numerical methods for quant

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recordType

award

status

SIGNED

region

EU

value

1499190

unit

EUR