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Career

· Research Intern, AWS Center for Quantum Computing2021–2021
· Research Intern, Google AI Quantum2020–2020
· Research Assistant, Institute for Quantum Information and Matter, Caltech2018–2024
· PhD 2024, California Institute of Technology

Publications (49)

Predicting many properties of a quantum system from very few measurements
Nature Physics · 2020
1,405
cited
Power of data in quantum machine learning
2020
846
cited
Challenges and opportunities in quantum machine learning
2022
651
cited
Quantum advantage in learning from experiments
Science · 2021
604
cited
Generalization in quantum machine learning from few training data
2021
476
cited
The randomized measurement toolbox
Nature Reviews Physics · 2022
387
cited
Provably efficient machine learning for quantum many-body problems
Science · 2021
292
cited
Information-theoretic bounds on quantum advantage in machine learning
Physical Review Letters · 2021
278
cited
Mixed-state entanglement from local randomized measurements
Physical Review Letters · 2020
223
cited
Efficient estimation of Pauli observables by derandomization
Physical Review Letters · 2021
163
cited
Preparing random states and benchmarking with many-body quantum chaos
2021
154
cited
Exponential Separations Between Learning With and Without Quantum Memory
2021
153
cited
Near-term quantum algorithms for linear systems of equations with regression loss functions
2019
152
cited
Emergent Quantum State Designs from Individual Many-Body Wave Functions
2021
118
cited
The complexity of NISQ
2022
116
cited
Out-of-distribution generalization for learning quantum dynamics
2022
108
cited
Learning many-body Hamiltonians with Heisenberg-limited scaling
2022
93
cited
Learning to predict arbitrary quantum processes
PRX Quantum · 2022
93
cited
Nearly tight Trotterization of interacting electrons
2020
68
cited
Improved machine learning algorithm for predicting ground state properties
Nature Communications · 2023
64
cited
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Hsin-Yuan Huang | Researcher Profile | Sotabase | Sotabase