Results 11 to 20 of about 137,793 (228)
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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Advances in quantum machine learning [PDF]
, 2015 Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental
Adcock, Jeremy +9 more
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On the Applicability of Quantum Machine Learning
Entropy, 2023 In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE).
Sebastian Raubitzek, Kevin Mallinger
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Shadows of quantum machine learning
Nature CommunicationsQuantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage.
Sofiene Jerbi +4 more
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Quantum Machine Learning—Quo Vadis?
EntropyThe book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience.
Andreas Wichert
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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
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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
Vedran Dunjko +2 more
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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
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Quantum Machine Learning: A tutorial
Neurocomputing, 2022 This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success ...
José David Martín-Guerrero +1 more
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Experimental Evaluation of Quantum Machine Learning Algorithms
IEEE Access, 2023 Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications.
Ricardo Daniel Monteiro Simoes +5 more
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