Quantum Machine Learning: What Quantum Computing Means to Data Mining

By Peter Wittek. 2014

425 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e8dccfb88a6524a67b6239f6b38a8fdaf15f6b39

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hanrui Wang
2. Yujun Lin
3. Song Han
4. Yongshan Ding
5. Jiaqi Gu
6. Ryan Larose
7. M. Krelina
8. Brian Coyle

Citations in the corpus

• Quantum technology for military applications (2021, background): “Quantum computing can possibly enhance, in principle, most classical ML/AI applications in defence; for example, automating cyber operations, algorithmic targeting, situation awareness and understanding and automated mission planning [182, 183].”
• 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].”
• Quantum machine learning (2016, unknown): “The analysis of their practical feasibility is a central subject of this review. As will be seen throughout the review, there are quantum algorithms for machine learning that exhibit quantum speedups [4, 5, 6, 7]. For example, the quantum basic linear algebra subroutines (BLAS)|Fourier transforms, n”
• Robust data encodings for quantum classifiers (2020, methodology): “These QNN applications, along with additional techniques and applications based on quantum kernel methods [39–42], define the emerging field of quantum machine learning (QML) [34, 43] 1 .”
• A systematic review on overfitting control in shallow and deep neural networks (2021, unknown): “When E Train ( f ) and E Test ( f ) are the error of model f on the training and the testing data, for each 𝜂 ∈ [ 0, 1 ] , the following probabilistic inequality is met (Wittek 2014): where h is VC-dimension of model f .”
• Quantum-enhanced machine learning (2016, background): “The recent results in QML have focused on improving computational complexity [5–8, 10 In this work we address this question, with emphasis on the more general, and less explored, RL setting.”
• Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “Also, in [171], fundamental learning concepts and the applications of QML have been comprehensively discussed, where some discussed notable QML tasks are quantum pattern recognition, quantum classification, quantum process tomography and regression, boosting QC, and adiabatic QC.”
• Quantum embeddings for machine learning (2020, background): “Machine learning is a potential application for near-term intermediate scale quantum computers [1–3].”
• Quantum computing for finance: Overview and prospects (2018, background): “This set of ideas has recently emerged with a lot of momentum [8, 52, 53].”