Speaker
Pooya Ronagh
Description
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 interest in quantum chemistry and quantum electrodynamics. We then show that incorporating classical shadow tomography in NQST significantly improves its learning of complex quantum states, and numerically demonstrate this advantage through case studies in atomic and condensed-matter physics.
| Keyword-1 | Quantum state tomography |
|---|---|
| Keyword-2 | Machine Learning |
