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Machine learning (ML) has the potential to become an essential tool in supporting clinical decision-making processes, offering enhanced diagnostic capabilities and personalized treatment plans. However, outsourcing medical records to train ML models using patient data raises legal, privacy, and security concerns. Federated learning has emerged as a promising paradigm for collaborative ML, meeting healthcare institutions' requirements for robust models without sharing sensitive data and compromising patient privacy. This study proposes a novel method that combines federated learning (FL) and Graph Neural Networks (GNNs) to predict stroke severity using electroencephalography (EEG) signals across multiple medical institutions. Our approach enables multiple hospitals to jointly train a shared GNN model on their local EEG data without exchanging patient information. Specifically, we address a regression problem by predicting the National Institutes of Health Stroke Scale (NIHSS), a key indicator of stroke severity. The proposed model leverages a masked self-attention mechanism to capture salient brain connectivity patterns and employs EdgeSHAP to provide post-hoc explanations of the neurological states after a stroke. We evaluated our method on EEG recordings from four institutions, achieving a mean absolute error (MAE) of 3.23 in predicting NIHSS, close to the average error made by human experts (MAE $\approx$ 3.0). This demonstrates the method's effectiveness in providing accurate and explainable predictions while maintaining data privacy., Comment: 13 pages, 5 figures, Proceedings of the II edition of the Workshop on Unifying Representations in Neural Models (UniReps 2024)

Early detection of atrial fibrillation (AFib) is challenging due to its asymptomatic and paroxysmal nature. However, advances in deep learning algorithms and the vast collection of electrocardiogram (ECG) data from devices such as the Internet of Things (IoT) hold great potential for the development of an effective solution. This study assesses the feasibility of training a neural network on a Federated Learning (FL) platform to detect AFib using raw ECG data. The performance of an advanced neural network is evaluated in centralized, local, and federated settings. The effects of different aggregation methods on model performance are investigated, and various normalization strategies are explored to address issues related to neural network federation. The results demonstrate that federated learning can significantly improve the accuracy of detection over local training. The best performing federated model achieved an F1 score of 77\%, improving performance by 15\% compared to the average performance of individually trained clients. This study emphasizes the promise of FL in medical diagnostics, offering a privacy-preserving and interpretable solution for large-scale healthcare applications.

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenomena. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS). We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. For each patient, we generated five graphs weighted by Lagged Linear Coherence (LLC) between signals from distinct Brodmann Areas, covering $\delta$ (2-4 Hz), $\theta$ (4-8 Hz), $\alpha_1$ (8-10.5 Hz), $\alpha_2$ (10.5-13 Hz), and $\beta_1$ (13-20 Hz) frequency bands. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties. We then trained a graph attention model to predict the NIHSS. By examining its attention coefficients, our model reveals insights into brain reconfiguration, providing clinicians with a valuable tool for diagnosis, personalized treatment, and early intervention in neurorehabilitation., Comment: 7 pages, 7 figures

Cooling of ions or other charged particles in electromagnetic traps is an essential tool to achieve control over their degrees of freedom on the quantum level. For many objects, there is no viable route for direct cooling, such as an accessible laser cooling transition. In such a case, the sympathetic cooling can be used, where a particle with such a direct route is used to cool down the other particle via Coulomb interaction. On the downside, this cooling process often is inefficient. Here, we numerically evaluate the sympathetic cooling performance in a quadrupole ion trap for different driving waveforms. We find that using different driving waveforms and optimized trap parameters the sympathetic cooling performance can be enhanced. These results will open up the way to achieve larger sympathetic cooling rates from which many techniques, such as aluminum ion clocks, might profit., Comment: 13 pages, 10 figures

The lack of interactions between single photons prohibits direct nonlinear operations in quantum optical circuits, representing a central obstacle in photonic quantum technologies. Here, we demonstrate multi-mode nonlinear photonic circuits where both linear and direct nonlinear operations can be programmed with high precision at the single-photon level. Deterministic nonlinear interaction is realized with a tunable quantum dot embedded in a nanophotonic waveguide mediating interactions between individual photons within a temporal linear optical interferometer. We demonstrate the capability to reprogram the nonlinear photonic circuits and implement protocols where strong nonlinearities are required, in particular for quantum simulation of anharmonic molecular dynamics, thereby showcasing the new key functionalities enabled by our technology., Comment: 8 pages, 3 figures

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed architecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication protocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and proposing mitigation strategies to increase the trustworthiness level.

Indistinguishability between photons is a key requirement for scalable photonic quantum technologies. We experimentally demonstrate that partly distinguishable single photons can be purified to reach near-unity indistinguishability by the process of quantum interference with ancillary photons followed by heralded detection of a subset of them. We report on the indistinguishability of the purified photons by interfering two purified photons and show improvements in the photon indistinguishability of $2.774(3)$\% in the low-noise regime, and as high as $10.2(5)$ \% in the high-noise regime., Comment: 14 pages, 7 figures

Fusion-based photonic quantum computing architectures rely on two primitives: i) near-deterministic generation and control of constant-size entangled states and ii) probabilistic entangling measurements (photonic fusion gates) between entangled states. Here, we demonstrate these key functionalities by fusing resource states deterministically generated using a solid-state spin-photon interface. Repetitive operation of the source leads to sequential entanglement generation, whereby curiously entanglement is created between the quantum states of the same spin at two different instances in time. Such temporal multiplexing of photonic entanglement provides a resource-efficient route to scaling many-body entangled systems with photons.

Boson sampling is a specialised algorithm native to the quantum photonic platform developed for near-term demonstrations of quantum advantage over classical computers. While clear useful applications for such near-term pre-fault-tolerance devices are not currently known, reaching a quantum advantage regime serves as a useful benchmark for the hardware. Here, we analyse and detail hardware requirements needed to reach quantum advantage with deterministic quantum emitters, a promising platform for photonic quantum computing. We elucidate key steps that can be taken in experiments to overcome practical constraints and establish quantitative hardware-level requirements. We find that quantum advantage is within reach using quantum emitters with an efficiency of 60%-70% and interferometers constructed according to a hybrid-mode-encoding architecture, constituted of Mach-Zehnder interferometers with an insertion loss of 0.0035 (a transmittance of 99.92%) per component.

We develop a quantum photonic platform that interconnects a high-quality quantum dot single-photon source and a low-loss photonic integrated circuit made in silicon nitride. The platform is characterized and programmed to demonstrate various multiphoton applications, including bosonic suppression laws and photonic entanglement generation. The results show a promising technological route forward to scale-up photonic quantum hardware.

Scalable photonic quantum computing architectures pose stringent requirements on photonic processing devices. The need for low-loss high-speed reconfigurable circuits and near-deterministic resource state generators are some of the most challenging requirements. Here we develop an integrated photonic platform based on thin-film lithium niobate and interface it with deterministic solid-state single-photon sources based on quantum dots in nanophotonic waveguides. The generated photons are processed with low-loss circuits programmable at speeds of several GHz. We realize a variety of key photonic quantum information processing functionalities with the high-speed circuits, including on-chip quantum interference, photon demultiplexing, and reprogrammability of a four-mode universal photonic circuit. These results show a promising path forward for scalable photonic quantum technologies by merging integrated photonics with solid-state deterministic photon sources in a heterogeneous approach to scaling up.