8 papers
Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications
M. Doris, S. Guo, S. M. Koh +5
Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-informed analysis techniques fo…
Can machine learning for quantum-gas experiments be explainable?
I. B. Spielman amd J. P. Zwolak
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for cla…
Repeated weak measurements: watching quantum correlations evolve
Emine Altuntas, Ian B. Spielman
Experimental access to many-body quantum systems is often limited by measurement backaction, and key dynamical properties are typically obtained by perturbing a system and measurin…
Imaginary gauge potentials in a non-Hermitian spin-orbit coupled quantum gas
Junheng Tao, Emmanuel Mercado-Gutierrez, Mingshu Zhao +1
In 1996, Hatano and Nelson proposed a non-Hermitian lattice model containing an imaginary Peierls phase [Phys. Rev. Lett. 77 570-573 (1996)], which subsequent analyses revealed to…
Nondestructive characterization of laser-cooled atoms using machine learning
G. De Sousa, M. Doris, D. D'Amato +3
We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light -- i.e., fluor…
Efficient production of sodium Bose-Einstein condensates in a hybrid trap
Yanda Geng, Shouvik Mukherjee, Swarnav Banik +6
We describe an apparatus that efficiently produces Na Bose-Einstein condensates (BECs) in a hybrid trap that combines a quadrupole magnetic field with a far-detuned optical…