Real-World Applications of Quantum-Enhanced Machine Learning Solutions DOI

Koduri Sreelakshmi,

Vishal V. Rathi,

K. Shanthi

et al.

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

Using machine learning in QSPR to estimate the boiling and critical temperatures of pure organic compounds DOI

Yassine Beghour,

Yasmina Lahiouel

Chemical Engineering Science, Journal Year: 2025, Volume and Issue: 309, P. 121228 - 121228

Published: Jan. 16, 2025

Language: Английский

Citations

0
Microscopic analysis of the differential low-temperature oxidation ability of coal DOI
Wei Sun, Yu Zhang, Fusheng Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136048 - 136048

Published: April 1, 2025

Language: Английский

Citations

0
Real-World Applications of Quantum-Enhanced Machine Learning Solutions DOI

Koduri Sreelakshmi,

Vishal V. Rathi,

K. Shanthi

et al.

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

Citations

0