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Photonic Quantum Computing Quantum Machine Learning

A Platform for Evanescently Trapping Rb-87 Using Silicon Nitride Strip Waveguides Buried in Silica

arXiv
Authors: Sam J. Harding, Carrie Weidner

Year

2025

Paper ID

16390

Status

Preprint

Abstract Read

~2 min

Abstract Words

93

Citations

N/A

Abstract

Cold-atom systems have emerged as a highly promising avenue for quantum-enhanced position, navigation, and timing applications. However, their wider adoption is currently hampered in part by the large footprint of the systems. In leveraging the miniaturisation possible through photonic integrated circuits, cold-atom sensors would be able to reach much wider commercial adoption. In this paper, we introduce a platform for evanescently trapping 87Rb using strip silicon nitride waveguides buried in silica using red- and blue-detuned fundamental and higher-order modes, providing a three-dimensional adjustable trap for BEC-based, chip-scale work in quantum science and technologies.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • Cold-atom systems have emerged as a highly promising avenue for quantum-enhanced position, navigation, and timing applications.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.01624 [2] arXiv https://arxiv.org/abs/2512.01624 [3] Publisher https://arxiv.org/abs/2512.01624

Local Citation Graph (Related-Paper Links)

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