Media Summary: Hsin-Yuan Huang, Sitan Chen and John Preskill. Hsin-Yuan Huang (Caltech) Panel discussion (1:06:00): Nathan Wiebe (University of Toronto), Ryan O'Donnell (Carnegie Mellon ... Recorded 18 October 2023. Hsin-Yuan (Robert) Huang of California Institute of Technology presents "

Learning To Predict Arbitrary Quantum - Detailed Analysis & Overview

Hsin-Yuan Huang, Sitan Chen and John Preskill. Hsin-Yuan Huang (Caltech) Panel discussion (1:06:00): Nathan Wiebe (University of Toronto), Ryan O'Donnell (Carnegie Mellon ... Recorded 18 October 2023. Hsin-Yuan (Robert) Huang of California Institute of Technology presents " Recorded 15 September 2023. Hsin-Yuan Huang (Robert) of Google Recorded 13 January 2026. Jiaqing Jiang of the University of California, Berkeley, presents " One of the most important applications in all of While

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QIP2023 | Learning to predict arbitrary quantum processes (Hsin-Yuan Huang)
Learning to Predict Arbitrary Quantum Processes
Hsin-Yuan (Robert) Huang - Learning to predict arbitrary quantum processes - IPAM at UCLA
Learning to predict arbitrary quantum processes
Quantum Advantage in Learning from Experiments | Qiskit Seminar Series
Robert Huang:  Fundamental aspects of solving quantum problems with machine learning
Hsin Yuan Huang (Caltech)
Hsin Yuan Huang (Robert) - Learning theory in the quantum universe - IPAM at UCLA
Hsin-Yuan Huang (Robert) - Classical ML for quantum problems - IPAM at UCLA
Provably Efficient Machine Learning for Quantum Many-Body Problems
Jiaqing Jiang - Predicting properties of quantum thermal states from a single trajectory
Quantum Machine Learning Explained
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QIP2023 | Learning to predict arbitrary quantum processes (Hsin-Yuan Huang)

QIP2023 | Learning to predict arbitrary quantum processes (Hsin-Yuan Huang)

Hsin-Yuan Huang, Sitan Chen and John Preskill.

Learning to Predict Arbitrary Quantum Processes

Learning to Predict Arbitrary Quantum Processes

Hsin-Yuan Huang (Caltech) Panel discussion (1:06:00): Nathan Wiebe (University of Toronto), Ryan O'Donnell (Carnegie Mellon ...

Hsin-Yuan (Robert) Huang - Learning to predict arbitrary quantum processes - IPAM at UCLA

Hsin-Yuan (Robert) Huang - Learning to predict arbitrary quantum processes - IPAM at UCLA

Recorded 18 October 2023. Hsin-Yuan (Robert) Huang of California Institute of Technology presents "

Learning to predict arbitrary quantum processes

Learning to predict arbitrary quantum processes

Title:

Quantum Advantage in Learning from Experiments | Qiskit Seminar Series

Quantum Advantage in Learning from Experiments | Qiskit Seminar Series

Quantum

Robert Huang:  Fundamental aspects of solving quantum problems with machine learning

Robert Huang: Fundamental aspects of solving quantum problems with machine learning

Machine

Hsin Yuan Huang (Caltech)

Hsin Yuan Huang (Caltech)

Title: Provably efficient machine

Hsin Yuan Huang (Robert) - Learning theory in the quantum universe - IPAM at UCLA

Hsin Yuan Huang (Robert) - Learning theory in the quantum universe - IPAM at UCLA

Recorded 15 September 2023. Hsin-Yuan Huang (Robert) of Google

Hsin-Yuan Huang (Robert) - Classical ML for quantum problems - IPAM at UCLA

Hsin-Yuan Huang (Robert) - Classical ML for quantum problems - IPAM at UCLA

Recorded 15 September 2023. Hsin-Yuan Huang (Robert) of Google

Provably Efficient Machine Learning for Quantum Many-Body Problems

Provably Efficient Machine Learning for Quantum Many-Body Problems

Hsin-Yuan Huang (Caltech) https://simons.berkeley.edu/talks/provably-efficient-machine-

Jiaqing Jiang - Predicting properties of quantum thermal states from a single trajectory

Jiaqing Jiang - Predicting properties of quantum thermal states from a single trajectory

Recorded 13 January 2026. Jiaqing Jiang of the University of California, Berkeley, presents "

Quantum Machine Learning Explained

Quantum Machine Learning Explained

IBM

The Quantum Algorithm That Could Make Big Pharma Billions

The Quantum Algorithm That Could Make Big Pharma Billions

One of the most important applications in all of #quantumcomputing While