Quantum materials are the cornerstones of many near-future applications including new computing chips, energy or quantum computing platforms. The success of these future applications depends heavily on a detailed understanding of the microscopic quantum phenomena at play. Unfortunately, the quantum advantage ultimately emanating from the entanglement between microscopic degrees of freedom comes with an exponential complexity when one needs to perform accurate modeling of quantum materials.

Within an ANR-funded collaboration between the Department of Physics, the University of Hong-Kong, the Université Toulouse 3—Paul Sabatier, and CY Cergy Paris Université, this theory project aims at developing and harnessing advanced computational techniques to fight this complexity and unveil the microscopic mechanisms in strongly entangled quantum matter. Our team develops state-of-the-art complementary numerical methods ranging from quantum Monte Carlo, exact diagonalization to tensor network and machine learning techniques, aiming at a unified computational paradigm for quantum material investigations.

We will address challenging computational situations, such as quantum systems with constraints or efficient measures of entanglement in quantum many-body systems. Precise physical systems to be investigated include frustrated magnets, strongly correlated fermionic systems or cold-atomic Rydberg arrays quantum simulators, with direct relevance to recent experimental developments.

Within this context, we are looking for a PhD student, based at the LPTM, CY Cergy Paris Université, to develop exact-diagonalization methods, in particular a quantum-typicality approach to dynamical properties at finite temperature, to benchmark the method, implement it in a high-performance code, and finally to apply it to experimentally relevant situations.