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

Quantum Machine Intelligence, Journal Year: 2025, Volume and Issue: 7(1)

Published: March 11, 2025

Language: Английский

---

Performance enhancement of artificial intelligence: A survey

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: unknown, P. 104034 - 104034

Published: Sept. 1, 2024

Language: Английский

---

Comparison of machine learning algorithms for classification of Big Data sets

Theoretical Computer Science, Journal Year: 2024, Volume and Issue: unknown, P. 114938 - 114938

Published: Oct. 1, 2024

Language: Английский

---

Quantum resonant dimensionality reduction

Physical Review Research, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 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

Language: Английский

---

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

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 167 - 187

Published: Jan. 1, 2025

Language: Английский

---

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

Biophysical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 15, 2025

Language: Английский

---

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

Quantum Machine Intelligence, Journal Year: 2025, Volume and Issue: 7(1)

Published: Feb. 25, 2025

Language: Английский

---

Real-World Applications of Quantum-Enhanced Machine Learning Solutions

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 81 - 106

Published: Feb. 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.

Language: Английский

---

Quantum Computing Methods for Malware Detection

Published: Jan. 1, 2025

Language: Английский

---

Learning Fourier series with parametrized quantum circuits

Physical Review Research, Journal Year: 2025, Volume and Issue: 7(2)

Published: May 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

Language: Английский

---