Quantum frequency conversion driven by classically non-separable light

Project funded by Research Council of Finland, grant 349120. Participating organisations named by the source: Tampere University. Funded amount: EUR 228,425, as stated by the source. Runs 2022-09-01 to 2024-12-31. Keywords: Physical sciences. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-07-23); national funding data, not CORDIS. Abstract given by the source: In this project I will investigate the frequency conversion of quantum states driven by classically non-separable vector beams, a novel process that can be termed Hybrid Quantum Frequency Conversion (HQFC). Vector beams, which show similar features as quantum mechanically entangled states, used as driving fields will enable the control the frequency conversion process such that an additional link between polarization states and spatial modes can be established. Benefiting from this new approach, I will be able to investigate hybrid quantum frequency conversions and novel entanglement mechanisms. I will further study the schemes scalability by invoking complex higher-order spatial modes. Thus, HQFC will pave a fresh path to controlling nonlinear processes for quantum photonics applications and aims at revealing the thin line between classical non-separability and genuine quantum entanglement.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Research Council of Finland. Retrieved 2026-10-28. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project "Quantum frequency conversion driven by classically non-separable light" is funded by the Research Council of Finland with a grant of EUR 228,425, running from September 1, 2022, to December 31, 2024, at Tampere University. Suggested technology pillar: sensing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.