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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"Quantum Machine Learning"”.
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Quantum Machine Learning Applications and Algorithms
L'abstract è presente nell'allegato / the abstract is in the attachment
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Towards Real-World Quantum Machine Learning
Quantum machine learning (QML) promises new representational and computational capabilities, yet practical deployment on near-term hardware is hampered by resource overheads, depth constraints, and fragile trainability. This thesis advances resource-aware QML by proposing architectures and kernels …
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Quantum Machine Learning Applied to Astronomical Datasets
This dissertation investigates the application of quantum machine learning techniques in the field of astronomy. The focus is on a variety of supervised and unsupervised tasks, including classification, clustering, and anomaly detection. Quantum kernel methods, such as quantum-enhanced support …
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The Trainability and Expressivity of Quantum Machine Learning Models
… more evidence that precise control of many-body quantum systems yields a method of computation more powerful than what is achievable using conventional models of computation. This culminated in recent years with experimental demonstrations on quantum devices of computational tasks on the verge of …
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Fundamentals of Quantum Communication Networks: Scalability, Efficiency, and Distributed Quantum Machine Learning
The future quantum Internet (QI) will transform today's communication networks and user experiences by providing unparalleled security levels, superior quantum computational powers, along with enhanced sensing accuracy and data processing capabilities. These features will be enabled through …
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Problem-Specific Quantum Machine Learning and Quantum Algorithms: Data Embeddings, Symmetries and Structure
… have contributed to the rise of the field of quantum computing over the last few decades. Quantum devices are becoming more powerful and more reliable than ever, poised to disrupt a variety of areas within both research and industry. Despite this promise, there are many challenges that need to …
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Enhancing Software Defect Prediction: Investigating Diverse Representations of Source Code as Feature Values in Classical and Quantum Machine Learning Approaches
… challenges because using some feature values in Machine/ Deep Learning (ML/ DL) models might seem infeasible and impractical. For instance, predicting whether a software component is buggy or non-buggy based on a feature like the ``age of the component" provides limited actionable insight for …
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Horizons of Artificial Intelligence in Quantum Computation
The potential emergence of practical quantum computers has guided research into their potential applications, particularly in the context of artificial intelligence. Motivated by the success of deep neural networks in classical machine learning, a prevailing hope is that such success will translate …
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Approximating a wavelet kernel using a quantum computer
Machine learning and quantum computing are both fields which have gained a significant amount of popularity and attention in recent years. The intersection of these two fields, quantum machine learning, looks at whether quantum computers can aid or improve classical machine learning methods, or …
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Spatial Quantum Computation in Graph Optimization Problems in Transportation Applications
… of life. This research explores the potential of Quantum Computing (QC) to address spatial optimization problems in transportation systems. By leveraging the principles of quantum mechanics, this research aims to enhance the efficiency and effectiveness of transportation networks through QC-based …
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Artificial Neural Networks for Programming Quantum Annealers
Quantum machine learning is an emerging field of research at the intersection of quantum computing and machine learning. It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum …
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Calibration and Utilization of High-Fidelity Two-Qubit Operations
… strides have been made in the field of quantum computing. Quantum advantage has been reported, and there is now an ecosystem of cloud-based quantum processors and companies interested in using them. However, high error rates continue to limit circuit depth, such that solving real-world …
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Novel Architectures for Radar Sounder Signals Segmentation: From Convolutional Neural Networks to Quantum-Enhanced Networks
… are grounded in hybrid supervised deep learning architectures, unsupervised feature learning frameworks, and harnessing quantum machine learning frameworks to automatically segment geological units in the cryosphere subsurface. Firstly, we developed a supervised deep learning …
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Quantum Algorithms for Solving Differential Equations with Application to Computational Fluid Dynamics
Quantum computing offers the potential to transform the way solutions to large-scale computational fluid dynamics (CFD) problems are obtained. This requires the development of efficient quantum algorithms for differential equations that are classically intractable. By bridging quantum mechanics …
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Re-imagining Drug Discovery with Quantum Computing: A Framework and Critical Benchmark Analysis for achieving Quantum Economic Advantage
Quantum computing’s (QC) promise of solving computationally hard problems has captured public attention and imagination, leading to significant private and public capital investments in recent years. At the same time, we are at the cusp of a biomedical revolution powered by computer-aided drug …
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Learning in Quantum Mechanics
This thesis explores the interactions of learning and quantum mechanics. At its heart, learning consists of extracting information from data. We will consider two types of data; random and deterministic. When data is random, one usually tries to learn an approximation to a desired object, when it …
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Evaluating Hybrid Quantum-Classical Models for Image Classification in the NISQ Era
… presents a systematic empirical evaluation of quantum machine learning performance under noisy intermediate-scale quantum (NISQ) era constraints. Through 670 controlled experiments, it evaluated quantum kernel support vector machines and variational quantum classifiers against classical …
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TOWARDS ACCELERATION OF QUANTUM-CLASSICAL ORCHESTRATION
Quantum computing is expected to act as a hardware accelerator within future computing infrastructures, complementing classical processors in the same way GPUs accelerate specific workloads today. While algorithms such as Shor’s and Grover’s suggest potential advantages, the practical use of …
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Investigating Topological Quantum Matter: Machine Learning Topological Phases, Topological Quantum Codes, Interplay of Disorder and Topology via Transport Phenomena and Phase Transitions
Future quantum technologies must meet three inter-locking demands: (i) faithful yet compact representations of strongly–entangled quantum matter, (ii) scalable error-mitigation protocols that tame spatially correlated noise, and (iii) near-term algorithms that coax useful optimisation and learning …