Conference Agenda

The quantum state of light in optical materials is well understood and can therefore be modeled with high accuracy. However, this task becomes increasingly challenging when complex photonic integrated circuits containing many components and multiple photons are considered. To address this challenge, I propose an EAD-like approach for quantum photonics. By applying various optimizations to computational physics models, it becomes possible to simulate circuits composed of multiple waveguides and photons efficiently. I will demonstrate the utility of this approach through several examples in which detailed information about photonic integrated circuits can be accurately extracted.

Quantum technology encompasses a broad range of platforms, each characterized by distinct physical mechanisms and experimental challenges. Over the past decades, several material systems have been developed to implement specific quantum functionalities; a central challenge for the advancement of quantum technologies is therefore the realization of direct interfaces between quantum nodes based on different platforms, enabling heterogeneous quantum networks.

In this work, we present a machine-learning-assisted design of an ion–photon interface based on spontaneous parametric down-conversion (SPDC) in a multilayer AlGaAs waveguide. The proposed device is engineered to generate narrowband, non-degenerate entangled photon pairs at 1092 nm—resonant with a transition in Sr ions—and at 1550 nm in the telecom C-band. This configuration enables entanglement distribution between a stationary ion qubit and a telecom-wavelength photonic flying qubit. The device therefore combines the long coherence times and high two-qubit gate fidelities of trapped Sr ions with the low transmission losses and robustness to noise of telecom photons, providing a promising route toward the implementation of hybrid quantum networks.

The machine-learning framework introduced here can be readily generalized by training the neural networks on alternative epitaxial structures, extending its applicability to a wide class of photonic devices and material platforms.

We investigate the effects of partial photon distinguishability on linear optical schemes that generate multi-photon states, specifically Greenberger-Horne-Zeilinger (GHZ) states and Bell states. We compute the error propagation by showing how the state fidelity decreases as a function of the partial distinguishability error, and compare the result for both states. We highlight simulation methods that were used in this work.

Photonics platforms combining laser-cooled atoms with optical nanofibres facilitate strong light–matter interactions across the classical and quantum regimes. The subwavelength confinement of the fibre-guided modes produces a large evanescent field, allowing efficient coupling of light to nearby atoms. We investigate atom-mediated processes in nanofibre-based platforms, with particular focus on Rydberg excitation of cold Rb-87 in close proximity to a dielectric. We identify key limitations to excitation efficiency and discuss alternative trapping strategies to overcome these effects.