96 citations · 207 across the 13 of their papers we have counts for
14 papers
Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms
Moises Ponce, Rebekah Herrman, Phillip C. Lotshaw +4
The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current nois…
Densely Connected -invariant Deep Neural Networks with Signed Permutation Representations
Devanshu Agrawal, James Ostrowski
We introduce and investigate, for finite groups , -invariant deep neural network (-DNN) architectures with ReLU activation that are densely connected-- i.e., include all p…
Approximate Boltzmann Distributions in Quantum Approximate Optimization
Phillip C. Lotshaw, George Siopsis, James Ostrowski +4
Approaches to compute or estimate the output probability distributions from the quantum approximate optimization algorithm (QAOA) are needed to assess the likelihood it will obtain…
A Classification of -invariant Shallow Neural Networks
Devanshu Agrawal, James Ostrowski
When trying to fit a deep neural network (DNN) to a -invariant target function with a group, it only makes sense to constrain the DNN to be -invariant as well. However, t…
A Group-Equivariant Autoencoder for Identifying Spontaneously Broken Symmetries
Devanshu Agrawal, Adrian Del Maestro, Steven Johnston +1
We introduce the group-equivariant autoencoder (GE-autoencoder) -- a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltoni…
Scaling Quantum Approximate Optimization on Near-term Hardware
Phillip C. Lotshaw, Thien Nguyen, Anthony Santana +5
The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial opt…