Conveners
(DCMMP) T4-7 Quantum Materials Symposium | Symposium sur les matériaux quantiques (DPMCM)
- Tamar Pereg-Barnea
- Tami Pereg-Barnea
Over the last years, artificial neural networks have been explored as powerful and systematically tuneable ansatz to represent quantum wave functions. Such numerical models can tomographically reconstruct quantum states and operator expectation values from a finite amount of measurements. At the same time, artificial neural networks can find the ground state wave function of a given...
Viewing neural quantum state tomography (NQST) as a flexible method for capturing classical snapshots of experimentally prepared quantum states opens doors to many applications of it in quantum simulation. In this talk we first review "Neural Error Mitigation" (Nat Mach Intell 4, 2022) for improving predictions of various observables obtained via quantum simulation of quantum states of...
In the past couple of years, machine learning has permeated many areas of physics and found numerous applications in condensed matter and chemistry. In particular, we have witnessed remarkable progress toward developing computational methods using neural networks as variational estimators. Variational representations of quantum states abound and have successfully been used to guess...
