no code implementations • 9 Apr 2024 • Wei Zi, Siyi Wang, Hyunji Kim, Xiaoming Sun, Anupam Chattopadhyay, Patrick Rebentrost

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers.

no code implementations • 26 Feb 2024 • Naixu Guo, Zhan Yu, Matthew Choi, Aman Agrawal, Kouhei Nakaji, Alán Aspuru-Guzik, Patrick Rebentrost

Generative machine learning methods such as large-language models are revolutionizing the creation of text and images.

no code implementations • 21 Dec 2023 • Joao F. Doriguello, Debbie Lim, Chi Seng Pun, Patrick Rebentrost, Tushar Vaidya

Finally, we propose a dequantised algorithm that also retains the polylogarithmic dependence on $n$, albeit with the linear scaling on $d$ from the standard LARS algorithm.

no code implementations • 25 Sep 2023 • Liming Zhao, Aman Agrawal, Patrick Rebentrost

Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected graphical models with visible and latent nodes, playing an important role in statistics and machine learning.

no code implementations • 17 Sep 2023 • Liming Zhao, Naixu Guo, Ming-Xing Luo, Patrick Rebentrost

Several works consider subclasses of quantum states that can be learned in polynomial sample complexity such as stabilizer states or high-temperature Gibbs states.

no code implementations • 20 Jul 2023 • Po-Wei Huang, Patrick Rebentrost

Hybrid quantum-classical computing in the noisy intermediate-scale quantum (NISQ) era with variational algorithms can exhibit barren plateau issues, causing difficult convergence of gradient-based optimization techniques.

no code implementations • 26 Mar 2020 • Yihui Quek, Clement Canonne, Patrick Rebentrost

We demonstrate a quantum algorithm for noisy quantum minimum-finding that preserves the quadratic speedup of the noiseless case: our algorithm runs in time $\tilde O(\sqrt{N (1+\Delta)})$, where $\Delta$ is an upper-bound on the number of elements within the interval $\alpha$, and outputs a good approximation of the true minimum with high probability.

no code implementations • 11 Jul 2019 • Yassine Hamoudi, Patrick Rebentrost, Ansis Rosmanis, Miklos Santha

Our classical result improves on the algorithm of [CLSW17] and runs in time $\widetilde{O}(n^{3/2}/\epsilon^2 \cdot \mathrm{EO})$.

2 code implementations • 29 Jun 2018 • Zhikuan Zhao, Alejandro Pozas-Kerstjens, Patrick Rebentrost, Peter Wittek

Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.

no code implementations • 1 Apr 2018 • Zhikuan Zhao, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, Joseph F. Fitzsimons

Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage.

no code implementations • 28 Nov 2016 • Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.

no code implementations • 1 Jul 2013 • Patrick Rebentrost, Masoud Mohseni, Seth Lloyd

Supervised machine learning is the classification of new data based on already classified training examples.

no code implementations • 1 Jul 2013 • Seth Lloyd, Masoud Mohseni, Patrick Rebentrost

Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces.

Quantum Physics

no code implementations • 1 Jul 2013 • Seth Lloyd, Masoud Mohseni, Patrick Rebentrost

The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyze the measurement results statistically.

Quantum Physics

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