Quantum machine learning: a systematic categorization based on learning paradigms, NISQ suitability, and fault tolerance

Quantum Machine Intelligence, Год журнала: 2025, Номер 7(1)

Опубликована: Март 11, 2025

Язык: Английский

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Performance enhancement of artificial intelligence: A survey

Journal of Network and Computer Applications, Год журнала: 2024, Номер unknown, С. 104034 - 104034

Опубликована: Сен. 1, 2024

Язык: Английский

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Comparison of machine learning algorithms for classification of Big Data sets

Theoretical Computer Science, Год журнала: 2024, Номер unknown, С. 114938 - 114938

Опубликована: Окт. 1, 2024

Язык: Английский

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Quantum resonant dimensionality reduction

Physical Review Research, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 3, 2025

Quantum computing is a promising candidate for accelerating machine learning tasks. Limited by the control accuracy of current quantum hardware, reducing consumption resources key to achieving advantage. Here, we propose resonant dimensionality reduction (QRDR) algorithm based on transition reduce dimension input data and accelerate algorithms. After QRDR, N can be reduced into desired scale R, effective information original will preserved correspondingly, which computational complexity subsequent algorithms or storage. QRDR operates with polylogarithmic time reduces error dependency from order 1/ε3 1/ε, compared existing Meanwhile, avoiding phase estimation, consumed qubits in independent ε. Therefore, algorithms, our has achieved optimal performance terms space complexity. We demonstrate combining two types classifiers, support vector machines convolutional neural networks, classifying underwater detection targets many-body phase, respectively. The simulation results indicate that extremely improved processing efficiency following application QRDR. As continues advance, potential utilized variety fields. Published American Physical Society 2025

Язык: Английский

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Quantum machine learning models in healthcare: future trends and challenges in healthcare

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 167 - 187

Опубликована: Янв. 1, 2025

Язык: Английский

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Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins

Biophysical Reviews, Год журнала: 2025, Номер unknown

Опубликована: Фев. 15, 2025

Язык: Английский

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Explaining quantum circuits with Shapley values: towards explainable quantum machine learning

Quantum Machine Intelligence, Год журнала: 2025, Номер 7(1)

Опубликована: Фев. 25, 2025

Язык: Английский

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Real-World Applications of Quantum-Enhanced Machine Learning Solutions

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 81 - 106

Опубликована: Фев. 28, 2025

QML is the quantum machine learning, a new approach to explore potential of computation allowing us discover solutions that otherwise would be hard for classical computer find. A variety applied areas will highlighted in following chapter such as optimization problems NLP, drug discovery. Some foundations quantum- superposition and entanglement- are designed provide more efficient approach- towards higher fidelity all data driven pipelines. This gives some practical use cases integration algorithms into pipelines learning well main challenges (i.e., regarding noise, scalability algorithm selection) concerning it. Hybrid quantum-classical schemes thought key progress practicality on currently available noisy intermediate-scale hardware. In this chapter, we address viewpoints operational strategy future, including disruptive effects future adoption insights QML.

Язык: Английский

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Quantum Computing Methods for Malware Detection

Опубликована: Янв. 1, 2025

Язык: Английский

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Learning Fourier series with parametrized quantum circuits

Physical Review Research, Год журнала: 2025, Номер 7(2)

Опубликована: Май 15, 2025

Variational quantum algorithms (VQAs) and their applications in the field of machine learning through parametrized circuits (PQCs) are thought to be one major way leveraging noisy intermediate-scale computing devices. However, differences performance certain VQA architectures often unclear since established best practices, as well detailed studies, missing. In this paper, we build upon work by Schuld [] Vidal comparing how popular ansatz for PQCs learn different one-dimensional truncated Fourier series. We also examine dissipative neural networks (dQNN) introduced Beer propose a data reupload structure dQNNs increase capability regression task. By results PQC architectures, can provide guidelines designing efficient PQCs. Published American Physical Society 2025

Язык: Английский

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