Results 11 to 20 of about 21,815 (240)
Quantum Machine Learning—Quo Vadis? [PDF]
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
doaj +4 more sources
Shadows of quantum machine learning [PDF]
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
doaj +7 more sources
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
exaly +11 more sources
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
Quantum Machine Learning: A Review and Case Studies [PDF]
Entropy, 2023 Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware.
Amine Zeguendry +2 more
doaj +2 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 located.
Chen SY, Yoo S.
europepmc +5 more sources
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.
europepmc +6 more sources
On the Applicability of Quantum Machine Learning [PDF]
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
doaj +2 more sources
Quantum machine learning beyond kernel methods. [PDF]
Nat Commun, 2023 AbstractMachine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models compare, both mutually and to classical models,
Jerbi S +5 more
europepmc +7 more sources
An optimizing method for performance and resource utilization in quantum machine learning circuits [PDF]
Scientific Reports, 2022 Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers.
Tahereh Salehi +4 more
doaj +2 more sources