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Free-space quantum key distribution (QKD) represents a groundbreaking advancement in secure communication, enabling secure key exchange over vast distances and offering robust encryption for the future quantum internet. However, the compatibility between fiber and free-space infrastructures continues to pose challenges for QKD protocols. Indeed, free-space and fiber-based networks commonly use different wavelengths and qubits encoding schemes. On the one hand, free-space QKD typically exploits visible light for its beneficial beam divergence compared to longer wavelengths, and polarization encoding for its robustness against turbulence. On the other hand, fiber-based QKD employs infrared light, particularly the C-band, because it shows the minimum losses with silica fibers, and time-bin encoding, due to polarization instability in optical fibers. In our study, we demonstrate the viability of a time-bin encoded QKD protocol operating in the C-band through horizontal turbulent free-space channels. We test the setup into a 50 m and a 500 m long links, achieving an average secure key rate of, respectively, 793 kbps and 40 kbps over several hours of measurements. The results encourage further exploration of the interoperability between free-space and fiber-based infrastructures, opening new possibilities for connecting terminal users with satellites in hybrid infrastructures., Comment: 10 pages, 6 figures

Bell measurements, which allow entanglement between uncorrelated distant particles, play a central role in quantum communication. Indeed sharing, measuring and creating entanglement lie at the core of various protocols, such as entanglement swapping and quantum teleportation. While for optical qubit systems a Bell measurement can be implemented using only linear components, the same result is no longer true for high-dimensional states, where one has to consider either ancillary photons or nonlinear processes. Here, inspired by the latter approach, we propose a protocol for high-dimensional quantum teleportation based on nonlinear techniques. Moreover, we discuss the practical implementation of our proposed setup in the case of path-encoded qutrits, where nonlinear effects arise from sum-frequency generation. Finally, we compute the fidelity between quantum states to benchmark the validity of our model under the presence of crosstalk noise. Our approach is deterministic, scalable and does not rely on the use of auxiliary photons, thus paving the way towards the practical implementation of quantum networks based on nonlinear effects., Comment: 9 pages, 3 figures

Entanglement is an essential ingredient in many quantum communication protocols. In particular, entanglement can be exploited in quantum key distribution (QKD) to generate two correlated random bit strings whose randomness is guaranteed by the nonlocal property of quantum mechanics. Most of QKD protocols tested to date rely on polarization and/or time-bin encoding. Despite compatibility with existing fiber-optic infrastructure and ease of manipulation with standard components, frequency-bin QKD have not yet been fully explored. Here we report the first demonstration of entanglement-based QKD using frequency-bin encoding. We implement the BBM92 protocol using photon pairs generated by two independent, high-finesse, ring resonators on a silicon photonic chip. We perform a passive basis selection scheme and simultaneously record sixteen projective measurements. A key finding is that frequency-bin encoding is sensitive to the random phase noise induced by thermal fluctuations of the environment. To correct for this effect, we developed a real-time adaptive phase rotation of the measurement basis, achieving stable transmission over a 26 km fiber spool with a secure key rate >= 4.5 bit/s. Our work introduces a new degree of freedom for the realization of entangled based QKD protocols in telecom networks.

Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing., Comment: 28 pages, 12 figures, submitted to IEEE Access

Event cameras have the ability to capture asynchronous per-pixel brightness changes, called "events", offering advantages over traditional frame-based cameras for computer vision applications. Efficiently coding event data is critical for transmission and storage, given the significant volume of events. This paper proposes a novel double deep learning-based architecture for both event data coding and classification, using a point cloud-based representation for events. In this context, the conversions from events to point clouds and back to events are key steps in the proposed solution, and therefore its impact is evaluated in terms of compression and classification performance. Experimental results show that it is possible to achieve a classification performance of compressed events which is similar to one of the original events, even after applying a lossy point cloud codec, notably the recent learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate reduction. Experimental results also demonstrate that events coded using JPEG PCC achieve better classification performance than those coded using the conventional lossy MPEG Geometry-based Point Cloud Coding standard. Furthermore, the adoption of learning-based coding offers high potential for performing computer vision tasks in the compressed domain, which allows skipping the decoding stage while mitigating the impact of coding artifacts.

The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs., Comment: Accepted at ICIP 2024

This paper leverages macroscopic models and multi-source spatiotemporal data collected from automatic traffic counters and probe vehicles to accurately estimate traffic flow and travel time in links where these measurements are unavailable. This problem is critical in transportation planning applications where the sensor coverage is low and the planned interventions have network-wide impacts. The proposed model, named the Macroscopic Traffic Estimator (MaTE), can perform network-wide estimations of traffic flow and travel time only using the set of observed measurements of these quantities. Because MaTE is grounded in macroscopic flow theory, all parameters and variables are interpretable. The estimated traffic flow satisfies fundamental flow conservation constraints and exhibits an increasing monotonic relationship with the estimated travel time. Using logit-based stochastic traffic assignment as the principle for routing flow behavior makes the model fully differentiable with respect to the model parameters. This property facilitates the application of computational graphs to learn parameters from vast amounts of spatiotemporal data. We also integrate neural networks and polynomial kernel functions to capture link flow interactions and enrich the mapping of traffic flows into travel times. MaTE also adds a destination choice model and a trip generation model that uses historical data on the number of trips generated by location. Experiments on synthetic data show that the model can accurately estimate travel time and traffic flow in out-of-sample links. Results obtained using real-world multi-source data from a large-scale transportation network suggest that MaTE outperforms data-driven benchmarks, especially in travel time estimation. The estimated parameters of MaTE are also informative about the hourly change in travel demand and supply characteristics of the transportation network., Comment: 34 pages, 28 figures, 6 tables

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data.

Photonic integrated circuits (PICs) are key in advancing quantum technologies for secure communications. They offer inherent stability, low losses and compactness compared to standard fiber-based and free-space systems. Our reasearch demonstrates PIC's effectivness in enhancing quantum communications, implementing a three-state BB84 protocol with decoy-state method. We employ an integrated receiver and superconducting nanowire single photon detectors (SNSPDs) to achieve technological advancements. One of the most notable results is the extraction of a secret key over a record-breaking 45 dB channel attenuation. Our results demonstrate a remarkable 220% boost in key rate compared to our prototype fiber-based receiver over a 10 dB channel attenuation. This improvement in the secret key rate (SKR) signifies the potential of integrated photonics to advance the field of quantum communication.

This document describes a deep learning-based point cloud geometry codec and a deep learning-based point cloud joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022. The proposed codecs are based on recent developments in deep learning-based PC geometry coding and offer some of the key functionalities targeted by the Call for Proposals. The proposed geometry codec offers a compression efficiency that outperforms the MPEG G-PCC standard and outperforms or is competitive with the V-PCC Intra standard for the JPEG Call for Proposals test set; however, the same does not happen for the joint geometry and colour codec due to a quality saturation effect that needs to be overcome., Comment: 52 pages, 8 figures. Document submitted to the 96th ISO/IEC JTC 1/SC 29/WG 1 (JPEG) meeting