A Machine Learning Approach to Generate Quantum Light

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

Keywords: quantum optics, high-dimensional quantum key distribution, quantum information processing, Hamiltonian, machine learning, physically-constrained

TL;DR: We present a physically-constrained and differentiable model for learning the relevant interaction parameters and designing nonlinear quantum optical systems that achieve desired D-dimensional quantum states.

Abstract: Spontaneous parametric down-conversion (SPDC) is a key technique in quantum optics used to generate entangled photon pairs. However, generating a desirable D-dimensional qudit state in the SPDC process remains a challenge. In this paper, we introduce a physically-constrained and differentiable model to overcome this challenge, and demonstrate its effectiveness through the design of shaped pump beams and structured nonlinear photonic crystals. We avoid any restrictions induced by the stochastic nature of our physical process and integrate a set of stochastic dynamical equations governing its evolution under the SPDC Hamiltonian. Our model is capable of learning the relevant interaction parameters and designing nonlinear quantum optical systems that achieve desired quantum states. We show, theoretically and experimentally, how to generate maximally entangled states in the spatial degree of freedom. Additionally, we demonstrate all-optical coherent control of the generated state by reshaping the pump beam. Our work has potential applications in high-dimensional quantum key distribution and quantum information processing.