About ALLEGRO ‘Agile ultra low energy secure networks’
ALLEGRO aims at designing and validating a novel end-to-end sliceable, reliable, and secure architecture for next-generation optical networks, achieving high transmission/switching capacity
- with 10 Tb/s for optoelectronic devices and 1 Pbt/s for optical fiber systems
- low power consumption/cost
- with > 25% savings
- and secure infrastructures and data transfers.
The architecture relies on key enabling innovations:
- smart, coherent transceivers exploiting multi-band & multi-fiber technologies for P2P and P2MP applications, based on e.g., high-speed plasmonic modulators/photodetectors and programmable silicon photonic integrated waveguide meshes;
- loss-less, energy-efficient transparent photonic integrated optical switches, eliminating OEO conversions, e.g., with on-chip amplification in the O-band for datacom applications;
- a consistent approach to security, in terms of functional/ protocol architectures and communications, further improving QKD systems, enabling optical channel co-existence and researching on quantum-resistant (post-quantum) cryptography, developing systems based on physically unclonable functions; and
- a scalable AI/ML assisted control and orchestration system, responsible for autonomous networking, dynamic and constrained service provisioning, function placement and resource allocation, leveraging devices increasing programmability and overall network softwarization.
To achieve the target objectives and KPIs, ALLEGRO has defined a clear methodology ending in ambitious demonstrators. The consortium includes a good balance of industry and research/academia with know-how in complementary fields.
The results of ALLEGRO will be disseminated in leading conferences, events, and high-impact journals. They will have a concrete and measurable economic and social impact, contributing towards achieving key European objectives, reinforcing European leadership and digital sovereignty in the ongoing digital and green transition.
Project News
New Publication: Polynomial Modeling of Noise Figure in EDFAs
A new paper published in MDPI Fibers introduces a polynomial-based model for accurately characterizing the noise figure of Erbium-Doped Fiber Amplifiers (EDFAs). The proposed model strikes a balance between accuracy and computational simplicity, making it well suited...
New Publication: Generating Realistic Optical Topologies with MoleNetwork
A new article published in the Journal of Optical Communications and Networking presents MoleNetwork, a methodology for generating realistic optical network topologies to support techno-economic analysis. The proposed framework enables researchers and network planners...
New Publication: Offline Scheduling for TDM-QKD Networks
We highlight a recent publication in the Journal of Optical Communications and Networking that investigates offline scheduling mechanisms for time-division multiplexed Quantum Key Distribution (TDM-QKD) networks. The paper focuses on how centralized, offline...
New Publication: Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
We are pleased to announce a new research article published in MDPI Fibers (2025): Polynomial Modeling of Noise Figure in Erbium-Doped Fiber AmplifiersR. D’Ingillo, A. Castronovo, S. Straullu, V. Curri In this work, the authors propose a polynomial model for the noise...
New Publication: Enhanced Network Security Protocols for the Quantum Era
We are pleased to announce a new article published in the IEEE Journal on Selected Areas in Communications (2025, Early Access): Enhanced Network Security Protocols for the Quantum Era: Combining Classical and Post-Quantum Cryptography, and Quantum Key...
New Publication: DRL-Assisted QoT-Aware Service Provisioning in Multi-Band Elastic Optical Networks
A new research article has been published in the Journal of Lightwave Technology (2025): DRL-Assisted QoT-Aware Service Provisioning in Multi-Band Elastic Optical NetworksAuthors: Y. Teng et al. This paper proposes a deep reinforcement learning–based framework for...
Learning the N-Input Parity Function with a Single-Qubit and Single-Measurement Sampling
We highlight a seminal contribution to quantum machine learning titled: Learning the N-Input Parity Function with a Single-Qubit and Single-Measurement Sampling This work addresses the problem of learning parity functions, a well-known hard task in classical learning...
New Publication on Hybrid Autoencoders for Variational Quantum Classifiers
A new research article titled “Enhancing the performance of variational quantum classifiers with hybrid autoencoders” has been published in Quantum Information Processing, Volume 24, Issue 8 (Article 244). The paper explores a hybrid classical–quantum learning...
Invited Paper on Optical Switching for Data Centers and Advanced Computing
A new invited article titled “Optical switching for data centers and advanced computing systems” has been published in the Journal of Optical Communications and Networking (JOCN), 2025. The paper reviews state-of-the-art optical switching architectures and their role...
New Publication on Time-Division Multiplexed QKD Networks
A new research article titled “Designing optimal Quantum Key Distribution Networks based on Time-Division Multiplexing of QKD transceivers: qTDM-QKDN” has been published in Future Generation Computer Systems (FGCS), Volume 164, 2025. The paper proposes qTDM-QKDN, an...