Poster
in
Workshop: From Molecules to Materials: ICLR 2023 Workshop on Machine learning for materials (ML4Materials)

Designing Nonlinear Photonic Crystals for High-Dimensional Quantum State Engineering

Eyal Rozenberg · Aviv Karnieli · Ofir Yesharim · Joshua Foley-Comer · Sivan Trajtenberg-Mills · Sarika Mishra · Shashi Prabhakar · Ravindra Singh · Daniel Freedman · Alex Bronstein · Ady Arie


Abstract:

We propose a novel, physically-constrained and differentiable approach for the generation of D-dimensional qudit states via spontaneous parametric down-conversion (SPDC) in quantum optics. We circumvent any limitations imposed by the inherently stochastic nature of the physical process and incorporate a set of stochastic dynamical equations governing its evolution under the SPDC Hamiltonian. We demonstrate the effectiveness of our model through the design of structured nonlinear photonic crystals (NLPCs) and shaped pump beams; and show, theoretically and experimentally, how to generate maximally entangled states in the spatial degree of freedom. The learning of NLPC structures offers a promising new avenue for shaping and controlling arbitrary quantum states and enables all-optical coherent control of the generated states. We believe that this approach can readily be extended from bulky crystals to thin Metasurfaces and potentially applied to other quantum systems sharing a similar Hamiltonian structures, such as superfluids and superconductors.

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