Results 11 to 20 of about 8,389,880 (297)
Quantum adversarial machine learning [PDF]
Physical Review Research, 2020 Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
doaj +4 more sources
Federated Quantum Machine Learning. [PDF]
Entropy (Basel), 2021 Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is ...
Chen SY, Yoo S.
europepmc +7 more sources
Quantum machine learning and quantum biomimetics: A perspective
Machine Learning: Science and Technology, 2020 Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to ...
Lamata Manuel, Lucas
core +5 more sources
Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
2025 IEEE International Symposium on Circuits and Systems (ISCAS)Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs ...
Chen, Samuel Yen-Chi +3 more
core +4 more sources
Subtleties in the trainability of quantum machine learning models [PDF]
, 2021 A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achieve a speedup over traditional machine learning for data analysis.
Thanasilp, Supanut +12 more
core +2 more sources
Quantum-Enhanced Machine Learning [PDF]
Physical Review Letters, 2016 5+15 pages. This paper builds upon and mostly supersedes arXiv:1507.08482. In addition to results provided in this previous work, here we achieve learning improvements in more general environments, and provide connections to other work in quantum machine learning. Explicit constructions of oracularized environments given in arXiv:1507.08482 are omitted
Vedran Dunjko +2 more
openaire +4 more sources
Quantum machine learning in ophthalmology. [PDF]
Eye (Lond)Masalkhi M, Ong J, Waisberg E, Lee AG.
europepmc +3 more sources
Quantum Machine Learning for Finance
CoRR, 2021 Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short
Marco Pistoia +12 more
openaire +3 more sources
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
IEEE Access, 2022 Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement ...
Danyal Maheshwari +2 more
doaj +1 more source
Quantum Machine Learning with SQUID
Quantum, 2022 In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient ...
Roggero, Alessandro +3 more
openaire +3 more sources