Quantum Photonics Optimization

The Quantum Photonics Optimization initiative explores the design of components in integrated quantum photonics using learning algorithms. Built upon solid theoretical foundations in quantum mechanics and optics, its goal is to establish a numerical simulation framework to model photonic devices. Furthermore, the initiative aims to develop an optimization pipeline where learning-based algorithms would automatically guide the exploration of the design space, utilizing quantum performance metrics—such as entanglement fidelity—calculated through these simulations as optimization parameters.

Optimization and Design of Photonic Devices

Research activities are organized into three main directions: (i) formulation of optimization models inspired by genetic algorithms, (ii) development of hybrid optimization frameworks, and (iii) evaluation of these methods on design and optimization problems for photonic devices. Particular attention is given to the optimization of physical and geometrical parameters for simulation and potential application in the design of quantum photonic circuits.

Current research focuses on optimizing model parameters and training strategies, aiming to improve convergence behavior, noise robustness, and interpretability of the resulting models. This approach seeks to advance the practical applicability of quantum optimization methods and contribute to the theoretical understanding of their advantages and limitations compared to classical optimization techniques.

Educational Tools for Quantum Circuit Simulation

During an initial stage of the project, the lack of educational tools that allow transparent and comprehensible simulation of quantum circuits for students with limited background in quantum computing was identified. As a preliminary result, a software for simulating quantum systems has been developed. This software allows explicit, step-by-step visualization of the system’s state evolution under the application of quantum operators, facilitating a deep understanding of the underlying physics of these devices and establishing a solid foundation for future stages of automated optimization of quantum photonic circuits.

For further details about this initative, please refer to the publications.