485 citations · 3k across the 63 of their papers we have counts for
9 papers · 1 filter
On nonlinear transformations in quantum computation
Zoë Holmes, Nolan Coble, Andrew T. Sornborger +1
While quantum computers are naturally well-suited to implementing linear operations, it is less clear how to implement nonlinear operations on quantum computers. However, nonlinear…
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…
Machine Learning Changes the Rules for Flux Limiters
Nga Nguyen-Fotiadis, Michael McKerns, Andrew Sornborger
Learning to integrate non-linear equations from highly resolved direct numerical simulations (DNSs) has seen recent interest for reducing the computational load for fluid simulatio…
Quantum simulation of operator spreading in the chaotic Ising model
Michael R. Geller, Andrew Arrasmith, Zoë Holmes +3
There is great interest in using near-term quantum computers to simulate and study foundational problems in quantum mechanics and quantum information science, such as the scramblin…
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
Alpha Renner, Forrest Sheldon, Anatoly Zlotnik +2
The capabilities of natural neural systems have inspired new generations of machine learning algorithms as well as neuromorphic very large-scale integrated (VLSI) circuits capable…
Universal compiling and (No-)Free-Lunch theorems for continuous variable quantum learning
Tyler Volkoff, Zoë Holmes, Andrew Sornborger
Quantum compiling, where a parameterized quantum circuit is trained to learn a target unitary, is an important primitive for quantum computing that can be used as a subroutine to o…