Generative Adversarial Learning Boosted by a Photonic Quantum Frequency Coprocessor
Philip Rübeling, Thomas Bækkegaard, Nikolaj Thomas Zinner, Michael Kues · 2023
Quantum generative learning is a promising candidate to demonstrate practical quantum advantage on state-of-the-art quantum information processing devices in the near future. In particular, photonic quantum frequency coprocessors (QFPs) [1] leverage quantum-correlated light sources, a high degree of mode scalability, robustness to decoherence and integration with preexisting telecom infrastructure. As was demonstrated experimentally in previous work [2], phase control and deterministic frequency mixing allow to manipulate individual frequency modes and provide coherent control of tens of frequency modes for two photons.