1.2k citations · 5.8k across the 35 of their papers we have counts for
35 papers
On quantum backpropagation, information reuse, and cheating measurement collapse
Amira Abbas, Robbie King, Hsin-Yuan Huang +4
The success of modern deep learning hinges on the ability to train neural networks at scale. Through clever reuse of intermediate information, backpropagation facilitates training…
The power and limitations of learning quantum dynamics incoherently
Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph +4
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share qu…
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with app…
Improved machine learning algorithm for predicting ground state properties
Laura Lewis, Hsin-Yuan Huang, Viet T. Tran +3
Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting…
Hardware-efficient learning of quantum many-body states
Katherine Van Kirk, Jordan Cotler, Hsin-Yuan Huang +1
Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science. Recent developments have shown that a modest number of randomi…
The Complexity of NISQ
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang +1
The recent proliferation of NISQ devices has made it imperative to understand their computational power. In this work, we define and study the complexity class , wh…
Learning many-body Hamiltonians with Heisenberg-limited scaling
Hsin-Yuan Huang, Yu Tong, Di Fang +1
Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this work, we propose the first algorithm to achieve the Heisenberg limit for learning an…
Learning to predict arbitrary quantum processes
Hsin-Yuan Huang, Sitan Chen, John Preskill
We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process over qubits. For a wide range of distributions on…
Foundations for learning from noisy quantum experiments
Hsin-Yuan Huang, Steven T. Flammia, John Preskill
Understanding what can be learned from experiments is central to scientific progress. In this work, we use a learning-theoretic perspective to study the task of learning physical o…
Dynamical simulation via quantum machine learning with provable generalization
Joe Gibbs, Zoë Holmes, Matthias C. Caro +5
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to e…
Out-of-distribution generalization for learning quantum dynamics
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell +5
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution gen…
The randomized measurement toolbox
Andreas Elben, Steven T. Flammia, Hsin-Yuan Huang +4
Increasingly sophisticated programmable quantum simulators and quantum computers are opening unprecedented opportunities for exploring and exploiting the properties of highly entan…
A Hierarchy for Replica Quantum Advantage
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang +1
We prove that given the ability to make entangled measurements on at most replicas of an -qubit state simultaneously, there is a property of which requires at least…
Revisiting dequantization and quantum advantage in learning tasks
Jordan Cotler, Hsin-Yuan Huang, Jarrod R. McClean
It has been shown that the apparent advantage of some quantum machine learning algorithms may be efficiently replicated using classical algorithms with suitable data access -- a pr…
Quantum advantage in learning from experiments
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler +8
Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data fro…
Exponential separations between learning with and without quantum memory
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang +1
We study the power of quantum memory for learning properties of quantum systems and dynamics, which is of great importance in physics and chemistry. Many state-of-the-art learning…
Generalization in quantum machine learning from few training data
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo +4
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a test…
What the foundations of quantum computer science teach us about chemistry
Jarrod R. McClean, Nicholas C. Rubin, Joonho Lee +5
With the rapid development of quantum technology, one of the leading applications is the simulation of chemistry. Interestingly, even before full scale quantum computers are availa…
Provably efficient machine learning for quantum many-body problems
Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai +2
Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML o…
Efficient estimation of Pauli observables by derandomization
Hsin-Yuan Huang, Richard Kueng, John Preskill
We consider the problem of jointly estimating expectation values of many Pauli observables, a crucial subroutine in variational quantum algorithms. Starting with randomized measure…
Emergent quantum state designs from individual many-body wavefunctions
Jordan S. Cotler, Daniel K. Mark, Hsin-Yuan Huang +5
Quantum chaos in many-body systems provides a bridge between statistical and quantum physics with strong predictive power. This framework is valuable for analyzing properties of co…
Preparing random states and benchmarking with many-body quantum chaos
Joonhee Choi, Adam L. Shaw, Ivaylo S. Madjarov +11
Producing quantum states at random has become increasingly important in modern quantum science, with applications both theoretical and practical. In particular, ensembles of such r…
On Liouville systems at critical parameters, Part 2: Multiple bubbles
Hsin-yuan Huang, Lei Zhang
In this paper, we continue to consider the generalized Liouville system: $$ Δ_g u_i+\sum_{j=1}^n a_{ij}ρ_j\left(\frac{h_j e^{u_j}}{\int h_j e^{u_j}}- {1} \right)=0\quad\text{in \,}…
Information-theoretic bounds on quantum advantage in machine learning
Hsin-Yuan Huang, Richard Kueng, John Preskill
We study the performance of classical and quantum machine learning (ML) models in predicting outcomes of physical experiments. The experiments depend on an input parameter and…