Potential and limitations of random Fourier features for dequantizing quantum machine learning

20 September 2023

Papers citing "Potential and limitations of random Fourier features for dequantizing quantum machine learning"

7 / 7 papers shown

Title
Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits Yuxuan Du Min-hsiu Hsieh Dacheng Tao 39 2 0 22 Aug 2024
On the relation between trainability and dequantization of variational quantum learning models Elies Gil-Fuster Casper Gyurik Adrián Pérez-Salinas Vedran Dunjko 34 10 0 11 Jun 2024
Prospects of Privacy Advantage in Quantum Machine Learning Jamie Heredge Niraj Kumar Dylan Herman Shouvanik Chakrabarti Romina Yalovetzky Shree Hari Sureshbabu Changhao Li Marco Pistoia 31 4 0 14 May 2024
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models Maximilian Wendlinger Kilian Tscharke Pascal Debus AAML 18 8 0 24 Apr 2024
On fundamental aspects of quantum extreme learning machines Weijie Xiong Giorgio Facelli Mehrad Sahebi Owen Agnel Thiparat Chotibut Supanut Thanasilp Zoë Holmes 24 23 0 23 Dec 2023
Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing M. Cerezo Martín Larocca Diego García-Martín N. L. Diaz Paolo Braccia ... Pablo Bermejo Aroosa Ijaz Supanut Thanasilp Eric R. Anschuetz Zoë Holmes 43 128 0 14 Dec 2023
On the expressivity of embedding quantum kernels Elies Gil-Fuster Jens Eisert Vedran Dunjko 32 16 0 25 Sep 2023