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SYML: Symposium Machine Learning in Atomic and Molecular Physics
SYML 1: Machine Learning in Atomic and Molecular Physics
SYML 1.3: Invited Talk
Tuesday, March 7, 2023, 12:00–12:30, E415
Machine-Learning assisted quantum computing and interferometry — •Ludwig Mathey1,2, Lukas Broers1, and Nicolas Heimann1,2 — 1Zentrum für Optische Quantentechnologien and Institut für Laserphysik, Universität Hamburg, 22761 Hamburg, Germany — 2The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany
In this talk I will discuss our recent work on developing machine-learning based algorithms to control the complexity of technologies such as quantum computing and high-precision interferometry.
In the context of quantum machine learning, I will first discuss our work on mitigating barren plateaus. Barren plateaus present a challenge to efficient quantum machine learning which derives from vanishing gradients of the objective function. We point out that parametrizations that are non-local in time, such as a Fourier mode representation of the parameter space, can noticeably improve the performance. As a second objective in the context of quantum machine learning, I will discuss algorithmic implementations directly aimed at concrete experimental platforms, towards optimal quantum algorithm realizations.
In context of machine-learning assisted interferometry, I will present our work that demonstrates improved interferometer operation aimed towards gravitational wave detection. Here, a key challenge is the reduction of noise of the interferometer mirrors, in particular in-situ. I will discuss our demonstration of in-situ seismic noise reduction, and our way forward.