Media Summary: Authors: Arthur Rattew, Po-Wei Huang, Naixu Guo, Lirandë Pira and Patrick Rebentrost Abstract: Fault-tolerant Quantum ... Speaker: Sofiene Jerbi Abstract: In this talk, I will present two recent works related to the question of quantum advantages in ... Speaker: Kouhei Nakaji Abstract: The convergence of artificial intelligence (AI) and quantum computing represents one of the ...

Qtml 2025 Accelerating Inference For - Detailed Analysis & Overview

Authors: Arthur Rattew, Po-Wei Huang, Naixu Guo, Lirandë Pira and Patrick Rebentrost Abstract: Fault-tolerant Quantum ... Speaker: Sofiene Jerbi Abstract: In this talk, I will present two recent works related to the question of quantum advantages in ... Speaker: Kouhei Nakaji Abstract: The convergence of artificial intelligence (AI) and quantum computing represents one of the ... Authors: Aiden Rosebush, Alexander Greenwood and Li Qian Abstract: We propose a machine learning based approach to ... Authors: Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert, Elies Gil-Fuster, Franz J. Schreiber and Carlos Bravo-Prieto ... Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam ...

Authors: Marco Ballarin, Juan José García-Ripoll, David Hayes and Michael Lubasch Abstract: Quantum state preparation of ... Authors: Nana Liu, Michele Minverini, Dhrumil Patel and Mark Wilde Abstract: In quantum thermodynamics, a system is described ... Speaker: Joseph Bowles Abstract: I will show how Fourier analysis can be used to construct quantum machine learning models ... Authors: Nicholas Rubin, Guanghao Low, Robbie King, Eugene DePrince, Alec White, Ryan Babbush, Dominic Berry and ... Authors: Stephen Jordan, Noah Shutty, Mary Wootters, Adam Zalcman, Alexander Schmidhuber, Robbie King, Sergei Isakov, ... About Intel Software: Intel® Developer Zone is committed to empowering and assisting software developers in creating ...

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QTML 2025: Accelerating Inference for Multilayer Convolutional Neural Networks
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
QTML 2025: A PAC-Bayesian Approach To Generalization For Quantum models
Faster LLMs: Accelerate Inference with Speculative Decoding
QTML 2025: Efficient quantum state preparation of multivariate functions using tensor networks
QTML 2025: Quantum thermodynamics and semi-definite optimization
QTML 2025: Scalable quantum machine learning models in Fourier space
QTML 2025:  Quantum Simulation By Sum Of Squares Spectral Amplification
QTML 2025: Decoded Quantum Interferometry
Efficient Large Language Model Inference with SqueezeLLM and KVQuant | Intel AI DevSummit 2025
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QTML 2025: Accelerating Inference for Multilayer Convolutional Neural Networks

QTML 2025: Accelerating Inference for Multilayer Convolutional Neural Networks

Authors: Arthur Rattew, Po-Wei Huang, Naixu Guo, Lirandë Pira and Patrick Rebentrost Abstract: Fault-tolerant Quantum ...

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 quantum advantages in ...

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 quantum computing represents one of the ...

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 machine learning based approach to ...

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 ...

Faster LLMs: Accelerate Inference with Speculative Decoding

Faster LLMs: Accelerate Inference with Speculative Decoding

Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam ...

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: Quantum state preparation of ...

QTML 2025: Quantum thermodynamics and semi-definite optimization

QTML 2025: Quantum thermodynamics and semi-definite optimization

Authors: Nana Liu, Michele Minverini, Dhrumil Patel and Mark Wilde Abstract: In quantum thermodynamics, a system is described ...

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 quantum machine learning models ...

QTML 2025:  Quantum Simulation By Sum Of Squares Spectral Amplification

QTML 2025: Quantum Simulation By Sum Of Squares Spectral Amplification

Authors: Nicholas Rubin, Guanghao Low, Robbie King, Eugene DePrince, Alec White, Ryan Babbush, Dominic Berry and ...

QTML 2025: Decoded Quantum Interferometry

QTML 2025: Decoded Quantum Interferometry

Authors: Stephen Jordan, Noah Shutty, Mary Wootters, Adam Zalcman, Alexander Schmidhuber, Robbie King, Sergei Isakov, ...

Efficient Large Language Model Inference with SqueezeLLM and KVQuant | Intel AI DevSummit 2025

Efficient Large Language Model Inference with SqueezeLLM and KVQuant | Intel AI DevSummit 2025

About Intel Software: Intel® Developer Zone is committed to empowering and assisting software developers in creating ...

Thinking Slow, Fast: Scaling Inference Compute (Feb 2025)

Thinking Slow, Fast: Scaling Inference Compute (Feb 2025)

Title: Thinking Slow, Fast: Scaling