Results 11 to 20 of about 154,173 (259)
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
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Quantum machine learning [PDF]
Nature, 2017 Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Jacob Biamonte +5 more
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Advanced Quantum TechnologiesThe meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the expectation that large-scale fault-tolerant machines will follow in the near to medium-term future, has led to much ...
S Karthikeyan +3 more
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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 located.
Chen SY, Yoo S.
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Quantum machine learning. [PDF]
Natl Sci Rev, 2019 We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs.
Allcock J, Zhang S.
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Quantum Reinforcement Learning with Quantum Photonics
Photonics, 2021 Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their ...
Lucas Lamata
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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
Dunjko, Vedran +2 more
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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
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Machine learning aided carrier recovery in continuous-variable quantum key distribution
npj Quantum Information, 2021 The secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is ...
Hou-Man Chin +4 more
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Quantum machine learning: from physics to software engineering
Advances in Physics: X, 2023 Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum ...
Alexey Melnikov +3 more
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