Quantum Machine Learning

By Maria Schuld, Francesco Petruccione. 2018

2,426 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ff8eea01cbb5de505672cf9bbda3a6a91624cf52

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Citations in the corpus

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, methodology): “The variational method has shown huge potentials in applications such as quantum machine learning [4], [7], [56], [63], numerical analysis [37], [39], quantum simulation [30], [31], [52], and optimizations [18], [47].”
Classification with Quantum Neural Networks on Near Term Processors (2018, background): “Other approaches to harnessing quantum resources in machine learning are reviewed here [3][4].”
Quantum Entanglement in Neural Network States (2017, background): “Very recently, these techniques have been introduced to many-body quantum condensed-matter physics, raising considerable interest across diﬀerent communities [22, 31–44].”
Potential of quantum computing for drug discovery (2018, background): “However, we note that such claims of quantum advantage are not without caveats [11, 151, 152], especially in light of recent works [153–155] showing classical algorithms with comparable performance. networks for classiﬁcation tasks [123].”
Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020, background): “Indeed, it is believed that the physical realization of qRAMmodel may be even much more difficult than the fault-tolerant quantum computers [104], [114].”
Experimental photonic quantum memristor (2021, background): “The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms [4, 5] to quantum machine learning [6, 7] and quantum neural networks [8].”
Quantum information processing with superconducting circuits: a review (2016, background): “Quantum machine learning (QML) [356] is now of great and increas ing interest in QIP [275, 356–362], in addition to classical ML optimisation processes of quantum hardware [281] and software [200, 363].”
A rigorous and robust quantum speed-up in supervised machine learning (2020, background): “Note that equation (1) is efficient in the dimension of H.”
Quantum machine learning: a classical perspective (2017, background): “We invite the reader to consult [7,8,187] for a review that includes these works.”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017, background): “For big data classification problems quite perspective and promising is a quantum computing approach [Biamonte et al., 2016, Chatterjee and Yu, 2016, Li et al., 2015, Rebentrost et al., 2014, Schuld et al., 2015] and a tensor network approach discussed in the next Chapter 3.”