Quantum Machine Learning: Recent Advances, Challenges, and Perspectives
This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve complex problems in different domains, leveraging quantum algorithms to enhance classical machine lea...
Saved in:
| Main Authors: | Pradeep Lamichhane, Danda B. Rawat |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11014055/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Entanglement detection with quantum support vector machine (QSVM) on near-term quantum devices
by: Mahmoud Mahdian, et al.
Published: (2025-04-01) -
Design and analysis of quantum machine learning: a survey
by: Linshu Chen, et al.
Published: (2024-12-01) -
An Overview of Quantum Machine Learning Research in China
by: Luning Li, et al.
Published: (2025-02-01) -
The Opportunities and Challenges in Integrating AI with Quantum Computing
by: Tomasz Słapczyński, et al.
Published: (2025-06-01) -
Quantum key distribution through quantum machine learning: a research review
by: Krupa Purohit, et al.
Published: (2025-05-01)