Berlin 2024 – scientific programme

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QI: Fachverband Quanteninformation

QI 27: Quantum Simulation II

QI 27.6: Talk

Thursday, March 21, 2024, 16:30–16:45, HFT-FT 101

Towards scalable simulations of correlated materials via hybrid quantum-classical algorithms — •Yannic Rath, Francois Jamet, Connor Lenihan, Lachlan P. Lindoy, Abhishek Agarwal, and Ivan Rungger — National Physical Laboratory, Teddington, TW11 0LW, United Kingdom

Dynamical mean-field theory (DMFT) has emerged as one of the main workhorses for the accurate numerical simulation of materials from first principles in regimes of strong correlation. In this talk, we discuss novel routes towards enhancing the predictive abilities of DMFT by integrating quantum algorithms into its pipeline in a hybrid quantum-classical approach [arXiv:2304.06587] and point out algorithmic advancements which are necessary to bring down the overall complexity of the method.

We leverage the representational power of tensor networks as a classical description of the Hamiltonian's ground state. To extract dynamical properties, we employ a quantum algorithm simulating a real-time evolution of the state [arXiv:2205.00094], which naturally increases the entanglement and limits the applicability of classical wavefunction methods. We discuss optimizations of the tensor network representation, as well as improvements to its compilation into a shallow quantum circuit, which tailors the approach for applications in practically relevant scenarios and increases its potential to eventually enable simulations that are out of reach of classical techniques.

Keywords: dynamical mean-field theory; hybrid algorithms; tensor networks; Anderson impurity model; strongly correlated materials