Publications

This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. We find that quantum models tend to outperform classical approaches as the problem complexity increases, as QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies.

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A multi-task deep-learning approach, inspired by computer vision's object detection, is proposed to simultaneously detect sleep events and construct hypnograms, streamlining the complex process of polysomnographic sleep analysis.

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We present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.

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Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. This study focuses on quantum kernel methods in the context of classification tasks. In particular, it examines the performance of quantum kernel estimation and quantum kernel training (QKT) in connection with two quantum feature mappings, namely, ZZFeatureMap and CovariantFeatureMap. Remarkably, these feature maps have been proposed in the literature under the conjecture of possible near-term quantum advantage and have shown promising performance in ad hoc datasets. This study aims to evaluate their versatility and generalization capabilities in a more general benchmark, encompassing both artificial and established reference datasets. Classical machine learning methods, specifically support vector machines and logistic regression, are also incorporated as baseline comparisons. Experimental results indicate that quantum methods exhibit varying performance across different datasets. Despite outperforming classical methods in ad hoc datasets, mixed results are obtained for the general case among standard classical benchmarks. The experimental data call into question a general added value of applying QKT optimization, for which the additional computational cost does not necessarily translate into improved classification performance. Instead, it is suggested that a careful choice of the quantum feature map in connection with proper hyperparameterization may prove more effective.

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This "Data Management Plan" document aims to set the lifecycle management plan for handling research data that will be collected, generated, and/or processed within the context of the project "Next Generation Machine Learning Algorithms for the Analysis of Medical Sleep Recordings" (NEXT-GEN-SOMNUS) according to the "FAIR data" and "as open as possible, as closed as necessary" principles.

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• Work Package 8

This work explores Quantum Kernel Estimation (QKE) and Quantum Kernel Training (QKT) in the context of classification tasks. Two quantum feature maps are analyzed for this purpose, in comparison to classical Support Vector Machine counterparts. Classification performance is analyzed on a selection of both ad-hoc and classical datasets, with QKT applied to optimize kernel parameters in QKE. Experimental data shows that quantum methods outperform classical ones in ad-hoc data. However, when confronting classical datasets, they frequently encounter difficulties in generalization, despite achieving high accuracy on the training set. We conclude that the choice of the feature mapping and the optimization of kernel parameters are critical for maximizing the effectiveness of the quantum methods.

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Accurate detection of sleep stages and sleep events is essential for the effective diagnosis and treatment of sleep disorders. However, current state-of-the-art methods often fall short in integrating these multiple tasks simultaneously. This study introduces a novel multi-task deeplearning approach for the joint detection of sleep events and hypnogram construction in a single pass. The proposed method adapts state-of-the-art single-shot object detection techniques to multi-channel time-series data, enabling simultaneous classification and detection of sleeping events. Experiments were conducted using diverse input channel combinations, including electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), oxygen saturation, airflow, and thoracic-abdominal movements, as well as multiple output configurations regarding sleep stages, EEG arousals, and respiratory events, both individually and combined. Results demonstrate improved accuracy in detecting respiratory events and sleep stages with additional channels. The multi-task approach enhanced overall performance, benefiting from shared knowledge across tasks.

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• Work Package 2