Hsin-Yuan (Robert) Huang: What cannot be learned in the Quantum Universe?

(1:4:21)

Complexity of Learning and Creating Quantum Systems - Hsin-Yuan (Robert) Huang

(44:13)

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

(1:14:9)

Hsin-Yuan \

(1:21:6)

Shallow Quantum Circuits - Hsin-Yuan (Robert) Huang

(1:35:13)

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

(25:31)

QIP2023 | Learning many-body Hamiltonians with Heisenberg-limited scaling (Hsin-Yuan Huang)

(27:59)

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

(50:23)

《梁祝》The Butterfly Lovers Concerto for Erhu | 二胡 | Conductor: Ke-Yuan Hsin, Erhu: Yun-Chen Tsai

(29:31)

Sibelius: Finlandia, Op. 26 | University of Texas University Orchestra (UTUO)

(9:2)

Franz Liszt - Les préludes, Symphonic Poem No. 3 | University of Texas University Orchestra

(14:42)

A tutorial on Quantum Approximate Optimization Algorithm (Oct 2020). Part 1: Theory

(52:38)

Dvorak: Carnival Overture, Op.92

(10:30)

Hsin-Yuan Shih - College Tennis Recruiting Video - Spring 2019

(4:29)

Quantum Advantage in Learning from Experiments | Qiskit Seminar Series

(1:17:42)

Bell's Inequality: The weirdest theorem in the world | Nobel Prize 2022

(13:22)

Recent Progress on Classical Shadow Tomography | Qiskit Quantum Seminar with Hong-Ye Hu

(1:59)

Provably Efficient Machine Learning for Quantum Many-Body Problems

(42:40)

IQIM Virtual Seminar, April 17, 2020 – Hsin-Yuan (Robert) Huang

(57:54)

QHack 2022: Hsin-Yuan Huang (Robert)—How powerful is classical AI from the standpoint of quantum AI?

(49:47)

QIP 2022 | Foundations for learning from noisy quantum experiments (Hsin-Yuan Huang)

(27:2)

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

(1:19:17)

Hsin Yuan Huang, Recent Advances in Predicting Properties of Quantum Systems

(39:39)

Nov. 2, 2022: Hsin Yuan (Robert) Huang (Caltech)

(1:56)

QIP2021 | Fundamental aspects of solving quantum problems with machine learning (Hsin-Yuan Huang)

(29:46)

Foundations for learning from noisy quantum experiments, Hsin Yuan (Robert) #QRST

(31:29)

QIP 2022 | Provably efficient machine learning for quantum many-body problems (Hsin-Yuan Huang)

(1:15)

2021-08-26 QML Meetup: Hsin-Yuan (Robert) Huang, Power of data in quantum machine learning

(1:1:20)