Quantifying high-dimensional spatial entanglement with a single-photon-sensitive time-stamping camera
Baptiste Courme, Chlo\'e Verni\`ere, Peter Svihra, Sylvain Gigan,, Andrei Nomerotski, Hugo Defienne

TL;DR
This paper demonstrates a method to quantify high-dimensional spatial entanglement using a single-photon-sensitive camera, enabling assumptions-free certification crucial for quantum information applications.
Contribution
It introduces a novel approach to measure high-dimensional entanglement without background subtraction or assumptions, advancing quantum certification techniques.
Findings
Position-momentum EPR correlations observed
Entanglement of formation exceeds 2.8
Dimensionality of entanglement is higher than 14
Abstract
High-dimensional entanglement is a promising resource for quantum technologies. Being able to certify it for any quantum state is essential. However, to date, experimental entanglement certification methods are imperfect and leave some loopholes open. Using a single-photon sensitive time-stamping camera, we quantify high-dimensional spatial entanglement by collecting all output modes and without background subtraction, two critical steps on the route towards assumptions-free entanglement certification. We show position-momentum Einstein-Podolsky-Rosen (EPR) correlations and quantify the entanglement of formation of our source to be larger than 2.8 along both transverse spatial axes, indicating a dimension higher than 14. Our work overcomes important challenges in photonic entanglement quantification and paves the way towards the development of practical quantum information processing…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Information and Cryptography · Laser-Matter Interactions and Applications · Neural Networks and Reservoir Computing
