Centre d'Etudes et De Recherche en Informatique et Communications

We present a quantum variational algorithm based on a novel circuit that generates all permutations that can be spanned by one- and two-qubits permutation gates. The construction of the circuits follows from group-theoretical results, most importantly the Bruhat decomposition of the group generated by the cx gates. These circuits require a number of qubits that scale logarithmically with the permutation dimension, and are therefore employable in near-term applications. We further augment the circuits with ancilla qubits to enlarge their span, and with these we build ansatze to tackle permutation-based optimization problems such as quadratic assignment problems, and graph isomorphisms. The resulting quantum algorithm, QuPer, is competitive with respect to classical heuristics and we could simulate its behavior up to a problem with 256 variables, requiring 20 qubits.

The increasing availability of Earth observation data offers unprecedented opportunities for large-scale environmental monitoring and analysis. However, these datasets are inherently heterogeneous, stemming from diverse sensors, geographical regions, acquisition times, and atmospheric conditions. Distribution shifts between training and deployment domains severely limit the generalization of pretrained remote sensing models, making unsupervised domain adaptation (UDA) crucial for real-world applications. We introduce FlowEO, a novel framework that leverages generative models for image-space UDA in Earth observation. We leverage flow matching to learn a semantically preserving mapping that transports from the source to the target image distribution. This allows us to tackle challenging domain adaptation configurations for classification and semantic segmentation of Earth observation images. We conduct extensive experiments across four datasets covering adaptation scenarios such as SAR to optical translation and temporal and semantic shifts caused by natural disasters. Experimental results demonstrate that FlowEO outperforms existing image translation approaches for domain adaptation while achieving on-par or better perceptual image quality, highlighting the potential of flow-matching-based UDA for remote sensing.

The $\mathbf{C O}_{\mathbf{2}}$ molecule, a greenhouse gas, is characterized by three vibrational modes: $i) v_{1}$, a symmetric stretching vibration, ii) $v_{2}$, a double degenerate symmetric bending mode and iii) $v_{3}$, an anti-symmetric stretching vibration, with absorption frequencies in the mid infrared range. In a recent publication, it was shown that at the harmonic level, the CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> molecule preserves its vibrational symmetry when trapped in nanocages of Clathrate or rare gas matrices with a degeneracy lifting of the bending mode. The symmetry of the molecule is preserved even for the fermi levels that originate from the interaction between fundamental $v_{1}$ and $2 v_{2}$ states when their frequency values coincide. This work, is an attempt to analyze the vibrational degrees of freedom of $\mathbf{C O}_{\mathbf{2}}$ from a numerical algorithm based on a neural network algorithm used as a tool to sample the electronic environment from the vibrational motion of the nuclei clamped to form the molecule either when it moves freely as in gas phase or under an electro-magnetic constraint when trapped in a nanocage. To achieve this goal, the molecule is characterized by its force constant as determined in the previous work rather than by its harmonic vibrational frequencies. Different recognition strategies will be shown through the application of standard neural network with back-propagation algorithm.

The Open Radio Access Network (O-RAN) paradigm has attracted considerable attention from both academic researchers and industry stakeholders, owing to its capacity to significantly enhance the adaptability and flexibility of cellular network architectures. However, the disaggregation of Generation NodeB (gNB) in O-RAN introduces increased system complexity and potential variability in performance metrics, necessitating empirical evaluation to determine whether its performance can rival or exceed that of conventional monolithic designs. In this study, we developed a standalone (SA) testbed to quantitatively assess the downlink (DL) transmission performance of two O-RAN architectures, implemented on two widely adopted open-source platforms: srsRAN and OAI. To ensure a scientifically robust and comprehensive analysis, we meticulously selected key performance metrics, implemented measures to provide solid statistical results and eliminate residual effects between test iterations. Test results show that the split enhances network flexibility by offloading certain processing tasks to the Distributed Unit (DU), thereby reducing the overall hardware usage burden and potentially improving the computational efficiency and resource allocation in future RAN networks.