A Comparison of Information Based Deflection Strategies. - Hsin-Yuan Huang; Thomas G. Robertazzi; Aurel A. Lazar (Comput. Networks ISDN Syst., 1995)

A 9.2b 47fJ/conversion-step asynchronous SAR ADC with input range prediction DAC switching. - Hsin-Yuan Huang; Jin-Yi Lin; Chih-Cheng Hsieh; Wen-Hsu Chang; Hann-Huei Tsai; Chin-Fong Chiu (ISCAS, 2012)

Video stabilization with distortion correction for wide-angle lens dashcam. - Hsin-Yuan Huang; Shuo-Han Yeh; Che-Tsung Lin; Shang-Hong Lai (APSIPA, 2016)

Lighting estimation from a single image containing multiple planes. - Ping-Cheng Kuo; Hsin-Yuan Huang; Shang-Hong Lai (MMSys, 2016)

Linear and Kernel Classification: When to Use Which? - Hsin-Yuan Huang; Chih-Jen Lin (SDM, 2016)

A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information. - Hsiang-Fu Yu; Hsin-Yuan Huang; Inderjit S. Dhillon; Chih-Jen Lin (AAAI, 2017)

FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension. - Hsin-Yuan Huang; Chenguang Zhu; Yelong Shen; Weizhu Chen (ICLR (Poster), 2018)

FlowQA: Grasping Flow in History for Conversational Machine Comprehension. - Hsin-Yuan Huang; Eunsol Choi; Wen-tau Yih (ICLR (Poster), 2019)

Predicting Features of Quantum Systems using Classical Shadows. - Hsin-Yuan Huang; Richard Kueng (CoRR, 2019)

Near-term quantum algorithms for linear systems of equations. - Hsin-Yuan Huang; Kishor Bharti; Patrick Rebentrost (CoRR, 2019)

Predicting Many Properties of a Quantum System from Very Few Measurements. - Hsin-Yuan Huang; Richard Kueng; John Preskill (CoRR, 2020)

Mixed-state entanglement from local randomized measurements. - Andreas Elben; Richard Kueng; Hsin-Yuan Huang; Rick van Bijnen; Christian Kokail; Marcello Dalmonte; Pasquale Calabrese; Barbara Kraus; John Preskill; Peter Zoller 001; Benoît Vermersch (CoRR, 2020)

Power of data in quantum machine learning. - Hsin-Yuan Huang; Michael Broughton; Masoud Mohseni; Ryan Babbush; Sergio Boixo; Hartmut Neven; Jarrod R. McClean (CoRR, 2020)

Exponential Separations Between Learning With and Without Quantum Memory. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (FOCS, 2021)

Information-theoretic bounds on quantum advantage in machine learning. - Hsin-Yuan Huang; Richard Kueng; John Preskill (CoRR, 2021)

Efficient estimation of Pauli observables by derandomization. - Hsin-Yuan Huang; Richard Kueng; John Preskill (CoRR, 2021)

Provably efficient machine learning for quantum many-body problems. - Hsin-Yuan Huang; Richard Kueng; Giacomo Torlai; Victor V. Albert; John Preskill (CoRR, 2021)

Generalization in quantum machine learning from few training data. - Matthias C. Caro; Hsin-Yuan Huang; Marco Cerezo; Kunal Sharma; Andrew T. Sornborger; Lukasz Cincio; Patrick J. Coles (CoRR, 2021)

A Hierarchy for Replica Quantum Advantage. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (CoRR, 2021)

Quantum advantage in learning from experiments. - Hsin-Yuan Huang; Michael Broughton; Jordan Cotler; Sitan Chen; Jerry Li 001; Masoud Mohseni; Hartmut Neven; Ryan Babbush; Richard Kueng; John Preskill; Jarrod R. McClean (CoRR, 2021)

Revisiting dequantization and quantum advantage in learning tasks. - Jordan Cotler; Hsin-Yuan Huang; Jarrod R. McClean (CoRR, 2021)

Nearly tight Trotterization of interacting electrons. - Yuan Su; Hsin-Yuan Huang; Earl T. Campbell (Quantum, 2021)

Out-of-distribution generalization for learning quantum dynamics. - Matthias C. Caro; Hsin-Yuan Huang; Nicholas Ezzell; Joe Gibbs; Andrew T. Sornborger; Lukasz Cincio; Patrick J. Coles; Zoë Holmes (CoRR, 2022)

Dynamical simulation via quantum machine learning with provable generalization. - Joe Gibbs; Zoë Holmes; Matthias C. Caro; Nicholas Ezzell; Hsin-Yuan Huang; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2022)

Foundations for learning from noisy quantum experiments. - Hsin-Yuan Huang; Steven T. Flammia; John Preskill (CoRR, 2022)

Learning many-body Hamiltonians with Heisenberg-limited scaling. - Hsin-Yuan Huang; Yu Tong 002; Di Fang 003; Yuan Su (CoRR, 2022)

The Complexity of NISQ. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (CoRR, 2022)

Learning to predict arbitrary quantum processes. - Hsin-Yuan Huang; Sitan Chen; John Preskill (CoRR, 2022)

Hardware-efficient learning of quantum many-body states. - Katherine Van Kirk; Jordan Cotler; Hsin-Yuan Huang; Mikhail D. Lukin (CoRR, 2022)

Challenges and opportunities in quantum machine learning. - Marco Cerezo; Guillaume Verdon; Hsin-Yuan Huang; Lukasz Cincio; Patrick J. Coles (Nat. Comput. Sci., 2022)

On quantum backpropagation, information reuse, and cheating measurement collapse. - Amira Abbas; Robbie King; Hsin-Yuan Huang; William J. Huggins; Ramis Movassagh; Dar Gilboa; Jarrod R. McClean (NeurIPS, 2023)

Improved machine learning algorithm for predicting ground state properties. - Laura Lewis; Hsin-Yuan Huang; Viet T. Tran; Sebastian Lehner; Richard Kueng; John Preskill (CoRR, 2023)

The power and limitations of learning quantum dynamics incoherently. - Sofiène Jerbi; Joe Gibbs; Manuel S. Rudolph; Matthias C. Caro; Patrick J. Coles; Hsin-Yuan Huang; Zoë Holmes (CoRR, 2023)

Tight bounds on Pauli channel learning without entanglement. - Senrui Chen; Changhun Oh; Sisi Zhou; Hsin-Yuan Huang; Liang Jiang (CoRR, 2023)

Learning quantum states and unitaries of bounded gate complexity. - Haimeng Zhao; Laura Lewis; Ishaan Kannan; Yihui Quek; Hsin-Yuan Huang; Matthias C. Caro (CoRR, 2023)

Certifying Almost All Quantum States with Few Single-Qubit Measurements. - Hsin-Yuan Huang; John Preskill; Mehdi Soleimanifar (FOCS, 2024)

Local Minima in Quantum Systems. - Chi-Fang Chen; Hsin-Yuan Huang; John Preskill; Leo Zhou (STOC, 2024)

Learning Shallow Quantum Circuits. - Hsin-Yuan Huang; Yunchao Liu 002; Michael Broughton; Isaac Kim 001; Anurag Anshu; Zeph Landau; Jarrod R. McClean (STOC, 2024)

Random unitaries in extremely low depth. - Thomas Schuster; Jonas Haferkamp; Hsin-Yuan Huang (CoRR, 2024)

Classically estimating observables of noiseless quantum circuits. - Armando Angrisani; Alexander Schmidhuber; Manuel S. Rudolph; Marco Cerezo; Zoë Holmes; Hsin-Yuan Huang (CoRR, 2024)

Predicting adaptively chosen observables in quantum systems. - Jerry Huang; Laura Lewis; Hsin-Yuan Huang; John Preskill (CoRR, 2024)

How to Construct Random Unitaries. - Fermi Ma; Hsin-Yuan Huang (IACR Cryptol. ePrint Arch., 2024)

Predicting quantum channels over general product distributions. - Sitan Chen; Jaume de Dios Pont; Jun-Ting Hsieh; Hsin-Yuan Huang; Jane Lange; Jerry Li 001 (COLT, 2025)

Learning shallow quantum circuits with many-qubit gates. - Francisco Vasconcelos 001; Hsin-Yuan Huang (COLT, 2025)

Unitary designs in nearly optimal depth. - Laura Cui; Thomas Schuster; Fernando Brandao; Hsin-Yuan Huang (CoRR, 2025)