96 citations · 207 across the 13 of their papers we have counts for
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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…
Solving MaxCut with Quantum Imaginary Time Evolution
Rizwanul Alam, George Siopsis, Rebekah Herrman +3
We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state i…
Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson +2
Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unwei…