Strong Light — Matter Interactions in a Bow-Tie Cavity and Machine Learning-Enhanced Cooling
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peters-mpeters1-phd-physics-2025-thesis.pdf
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Author(s)
Peters, Matthew
Advisor(s)
Vuletić, Vladan
Date Issued
September 2025
Publisher
Massachusetts Institute of Technology
Abstract
Neutral atoms in optical tweezers have emerged as a highly promising platform, offering long intrinsic coherence times, the ability to arrange qubits in flexible, reconfigurable geometries, and the capacity for fast entangling gates mediated by strong Rydberg interactions. However, their advancement towards large-scale quantum processing is often impeded by several key limitations. These include slow or destructive qubit readout methodologies, probabilistic array preparation which necessitates time-consuming atom rearrangement, and cooling techniques that can be dependent on specific atomic internal structures or require complex experimental geometries. This thesis addresses these critical challenges by detailing the first applications of a novel experimental system that integrates an array of neutral cesium atoms with a high-cooperativity (𝜂 > 20) four-mirror optical bow-tie cavity. We first leverage our optical resonator to demonstrate fast, non-destructive, and number-resolved readout of individual atoms. This capability enables the real-time observation of light-assisted collisional dynamics and underpins an adaptive feedback protocol that achieves quasi-deterministic single-atom loading with a 92(2)% success probability. Building on this robust control, we utilize the cavity’s unique properties—including a narrow linewidth and intrinsic running-wave modes—to realize efficient three-dimensional cooling near the ground state and report the first demonstration of programmable few-atom Bragg scattering in a bow-tie resonator. To address the overarching challenge of optimizing complex, high-dimensional experimental systems, we then pioneer the use of machine learning for atomic cooling. We introduce a deep reinforcement learning agent, trained via a sim-to-real protocol, that learns to actively cool a single atom more rapidly than conventional methods. In a separate demonstration, we apply Bayesian optimization to the multi-parameter landscape of magneto-optical trap compression and polarization gradient cooling to produce a Bose-Einstein condensate directly from polarization gradient cooling. Collectively, these results establish the integration of bow-tie cavities and machine intelligence as a powerful paradigm for building the next generation of robust, scalable, and high-fidelity neutral-atom quantum processors.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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