Network Design in Multiple Non-Overlapping Paths within Quantum Key Distribution Networks
The increasing demand for secure communication makes Quantum Key Distribution (QKD) a compelling technology, as it exploits the fundamental principles of quantum physics to enhance secure data transmission. However, practical deployment faces limitations such as limited transmission distances and dedicated hardware-channel requirements. This paper proposes a linear programming-based model that jointly addresses routing, wavelength assignment, and trusted-node minimization in QKD networks with wavelength division multiplexing (WDM), considering that the secret key rate becomes impractical beyond a given transmission distance, thus requiring the relaying of secret keys through trusted repeaters. In particular, the study contrasts scenarios where traffic is concentrated along a single route with those where key rates are split and routed over multiple nonoverlapping paths (MNOPs) using two mechanisms: Concatenate Multipart (CM) and Bitwise Product Multipath (BPM). While the literature shows that MNOPs can improve security (BPM) and improve the secret key rate (CM), we demonstrate that these mechanisms come with additional deployment costs.
Photonic Computing Towards Energy-Efficient AI Solutions Based on Photoactivated Materials
Artificial intelligence (AI) and neuromorphic computing are driving the development of hardware platforms that combine high energy efficiency with physically informed data processing. Photonic technologies offer distinct advantages, particularly when coupled with photoactivated materials that allow light-matter interactions to be directly exploited for computation. Inspired by synaptic mechanisms in biological systems, a neuromorphic photonic layer based on a luminescent material was developed to mimic synaptic memory via the material’s optical afterglow. The information-processing capability of the photonic front-end was evaluated by coupling its output responses to a digital machine learning model, achieving 76.9% test accuracy on the MNIST handwritten digit recognition task. This approach demonstrates the potential of hardware AI systems that tightly integrate material-level functionalities with neuromorphic computation by exploiting synapse-like optical memory in luminescent materials.
Angular-resolved nonlinear optical response as a probe of Lorentz violation in noncentrosymmetric materials
We propose a methodology to detect weak Lorentz-violating (LV) backgrounds through the nonlinear shift photocurrent in noncentrosymmetric crystals. Using a spinful Rice–Mele model, we show that a LV background induces a momentum-odd correction to the Bloch Hamiltonian that reshapes the phase of the interband dipole matrix elements. As a result, the shift conductivity develops a robust π-periodic modulation as a function of the angle of a perpendicularly applied static electric field, in contrast to a weakly 2π-periodic response of the Lorentz-symmetric case. This change in angular periodicity provides a signature of LV effects which can be directly identified through a photocurrent measurement. For realistic optical intensities, the predicted signal lies in the picoampere range, which can be enhanced in a matrix of weakly interacting chains, allowing sensitivity to LV coupling strengths of the order of ξ∼10−24Cm. These results establish nonlinear optical transport as a viable probe of emergent LV effects in solid-state systems.