activity
20242026
collaborators

6 papers

physics.optics2026

Universal Neural Network Based Calibration and Control of Programmable Classical and Quantum Photonic Integrated Processors

José Roberto Rausell-Campo, Daniele Melati, Bhavin Shastri +2

Efficient calibration and control of programmable photonic integrated circuits are fundamental for scaling quantum and classical optical computing processors. While neural network-…

physics.optics2026

Energy-efficient programmable integrated photonics via optimized Euler rotations

Pablo Martínez-Carrasco Romero, Andrés Macho-Ortíz, José Roberto Rausell-Campo +2

Programmable integrated photonics (PIP) has emerged as a powerful on-chip platform for optical signal processing and computing, enabling the implementation of reconfigurable N$\tim…

physics.optics2026

High-Speed Non-Volatile Barium Titanate Field Programmable Photonic Gate Array

Cristina Catalá-Lahoz, Jose Roberto Rausell-Campo, Daniel Pérez-López +10

Programmable integrated photonics aims to replicate the versatility of field-programmable gate arrays in the optical domain. However, scaling these systems has been prevented by th…

physics.optics2025

Ising accelerator with a reconfigurable interferometric photonic processor

José Roberto Rausell-Campo, Nayem Al Kayed, Daniel Pérez-Lppez +3

The general-purpose programmable photonic processors offer a scalable and reconfigurable solution for a wide range of RF and optical applications. Therefore, implementing photonic…

physics.optics2024

Programming universal unitary transformations on a general-purpose silicon photonics platform

Jose Roberto Rausell-Campo, Daniel Pérez, López +1

General-purpose programmable photonic processors provide a versatile platform for integrating diverse functionalities on a single chip. Leveraging a two-dimensional hexagonal waveg…

physics.optics2024

Programmable Photonic Extreme Learning Machines

Jose Roberto Rausell-Campo, Antonio Hurtado, Daniel Pérez-López +1

Photonic neural networks offer a promising alternative to traditional electronic systems for machine learning accelerators due to their low latency and energy efficiency. However,…