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…
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…
Multi-angle Quantum Approximate Optimization Algorithm
Rebekah Herrman, Phillip C. Lotshaw, James Ostrowski +2
The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by paramet…
Globally optimizing QAOA circuit depth for constrained optimization problems
Rebekah Herrman, Lorna Treffert, James Ostrowski +3
We develop a global variable substitution method that reduces -variable monomials in combinatorial optimization problems to equivalent instances with monomials in fewer variable…
Empirical performance bounds for quantum approximate optimization
Phillip C. Lotshaw, Travis S. Humble, Rebekah Herrman +2
The quantum approximate optimization algorithm (QAOA) is a variational method for noisy, intermediate-scale quantum computers to solve combinatorial optimization problems. Quantify…
Impact of Graph Structures for QAOA on MaxCut
Rebekah Herrman, Lorna Treffert, James Ostrowski +3
The quantum approximate optimization algorithm (QAOA) is a promising method of solving combinatorial optimization problems using quantum computing. QAOA on the MaxCut problem has b…
Integer Programming from Quantum Annealing and Open Quantum Systems
Chia Cheng Chang, Chih-Chieh Chen, Christopher Koerber +2
While quantum computing proposes promising solutions to computational problems not accessible with classical approaches, due to current hardware constraints, most quantum algorithm…
Lower Bounds on Circuit Depth of the Quantum Approximate Optimization Algorithm
James Ostrowski, Rebekah Herrman, Travis S. Humble +1
The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of co…