Media Summary: Speaker: Sofiene Jerbi Abstract: In this talk, I will present two recent works related to the question of Speaker: Kouhei Nakaji Abstract: The convergence of artificial intelligence (AI) and Authors: Aiden Rosebush, Alexander Greenwood and Li Qian Abstract: We propose a

Qtml 2025 Shadows Of Quantum Machine Learning And Shallow Depth Learning Separations - Detailed Analysis & Overview

Speaker: Sofiene Jerbi Abstract: In this talk, I will present two recent works related to the question of Speaker: Kouhei Nakaji Abstract: The convergence of artificial intelligence (AI) and Authors: Aiden Rosebush, Alexander Greenwood and Li Qian Abstract: We propose a Authors: Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert, Elies Gil-Fuster, Franz J. Schreiber and Carlos Bravo-Prieto ... Speaker: Matthias Caro Abstract: In this tutorial, we will explore how techniques from property testing and interactive proofs can be ... Authors: William J. Huggins, Tanuj Khattar and Nathan Wiebe Abstract: For many practical applications of

Authors: Adrián Pérez-Salinas, Patrick Emonts, Jordi Tura Brugués and Vedran Dunjko Abstract: Classical simulation of Authors: Marco Ballarin, Juan José García-Ripoll, David Hayes and Michael Lubasch Abstract: Speaker: Joseph Bowles Abstract: I will show how Fourier analysis can be used to construct Sergeii Strelchuk from the University of Oxford describing

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QTML 2025: Shadows of quantum machine learning and shallow-depth learning separations
QTML 2025: AI for Quantum: Toward AI-Enhanced Quantum Computing Applications
QTML 2025: A Universal Script for Machine Learning Derived Entanglement Witnesses
Quantum Machine Learning Explained
QTML 2025: A PAC-Bayesian Approach To Generalization For Quantum models
QTML 2025: Testing and Verification for Quantum Learning
QTML 2025: Productionizing Quantum Mass Production
QTML 2025: Multiple-Basis Representation Of Quantum States
QTML 2025: Quantum Generative Modeling Beyond the NISQ Era
QTML 2025: Efficient quantum state preparation of multivariate functions using tensor networks
QTML 2025: Scalable quantum machine learning models in Fourier space
QTML Tutorial by Sergeii Strelchuk - Quantum Computing Learning Theory
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QTML 2025: Shadows of quantum machine learning and shallow-depth learning separations

QTML 2025: Shadows of quantum machine learning and shallow-depth learning separations

Speaker: Sofiene Jerbi Abstract: In this talk, I will present two recent works related to the question of

QTML 2025: AI for Quantum: Toward AI-Enhanced Quantum Computing Applications

QTML 2025: AI for Quantum: Toward AI-Enhanced Quantum Computing Applications

Speaker: Kouhei Nakaji Abstract: The convergence of artificial intelligence (AI) and

QTML 2025: A Universal Script for Machine Learning Derived Entanglement Witnesses

QTML 2025: A Universal Script for Machine Learning Derived Entanglement Witnesses

Authors: Aiden Rosebush, Alexander Greenwood and Li Qian Abstract: We propose a

Quantum Machine Learning Explained

Quantum Machine Learning Explained

IBM

QTML 2025: A PAC-Bayesian Approach To Generalization For Quantum models

QTML 2025: A PAC-Bayesian Approach To Generalization For Quantum models

Authors: Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert, Elies Gil-Fuster, Franz J. Schreiber and Carlos Bravo-Prieto ...

Sponsored
QTML 2025: Testing and Verification for Quantum Learning

QTML 2025: Testing and Verification for Quantum Learning

Speaker: Matthias Caro Abstract: In this tutorial, we will explore how techniques from property testing and interactive proofs can be ...

QTML 2025: Productionizing Quantum Mass Production

QTML 2025: Productionizing Quantum Mass Production

Authors: William J. Huggins, Tanuj Khattar and Nathan Wiebe Abstract: For many practical applications of

QTML 2025: Multiple-Basis Representation Of Quantum States

QTML 2025: Multiple-Basis Representation Of Quantum States

Authors: Adrián Pérez-Salinas, Patrick Emonts, Jordi Tura Brugués and Vedran Dunjko Abstract: Classical simulation of

QTML 2025: Quantum Generative Modeling Beyond the NISQ Era

QTML 2025: Quantum Generative Modeling Beyond the NISQ Era

Speaker: Michele Grossi Abstract:

QTML 2025: Efficient quantum state preparation of multivariate functions using tensor networks

QTML 2025: Efficient quantum state preparation of multivariate functions using tensor networks

Authors: Marco Ballarin, Juan José García-Ripoll, David Hayes and Michael Lubasch Abstract:

QTML 2025: Scalable quantum machine learning models in Fourier space

QTML 2025: Scalable quantum machine learning models in Fourier space

Speaker: Joseph Bowles Abstract: I will show how Fourier analysis can be used to construct

QTML Tutorial by Sergeii Strelchuk - Quantum Computing Learning Theory

QTML Tutorial by Sergeii Strelchuk - Quantum Computing Learning Theory

Sergeii Strelchuk from the University of Oxford describing