709 citations · 2.4k across the 19 of their papers we have counts for
32 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…