96 citations · 270 across the 28 of their papers we have counts for
32 papers
Two-Stage Quantum-Classical Distribution Network Reconfiguration via Cycle-Edge Encoding
Nowar Alashkar, Cade Kennedy, Phillip C. Lotshaw +6
Distribution network reconfiguration (DNR) is a combinatorial optimization problem that seeks a low-loss network topology subject to topological and electrical operating constraint…
Warm-Start Iterative QITE for Distribution Network Reconfiguration via Branch-Exchange Encoding
Noah Crum, Pete Pritchard, Tanner Rase +5
We present a hybrid quantum-classical algorithm for distribution network reconfiguration, a combinatorial optimization problem on power distribution networks, that combines a radia…
Probing two-spin entanglement at quantum criticality on a quantum processor
Anshumitra Baul, Xiao Xiao, Phillip C. Lotshaw
Quantum phase transitions in many-body systems give rise to highly entangled states, and understanding their quantum correlations is crucial for characterizing quantum materials. H…
Emergence of synthetic twist defects in the surface code under local perturbation
Paul Kairys, Phillip C. Lotshaw
Topologically-ordered quantum states with Abelian excitations can host defects that obey effective non-Abelian statistics, in principle allowing for quantum information processing…
QAOA Parameter Transfer for Hypergraphs
Lucas T. Braydwood, Phillip C. Lotshaw
Variational Quantum Algorithms, including the Quantum Approximate Optimization Algorithm (QAOA), have shown promise in solving optimization problems but rely on costly variational…
Iterative warm-start optimization with quantum imaginary time evolution
Phillip C. Lotshaw, Titus Morris, Stuart Hadfield +1
Approximate combinatorial optimization is a promising use case for quantum computers. The quantum optimization algorithms often employ a fixed ansatz that evolves an unbiased initi…
The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
Gilles Buchs, Thomas Beck, Ryan Bennink +19
Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic…
Robust Chiral Edge Dynamics of a Kitaev Honeycomb on a Trapped Ion Processor
Ammar Ali, Joe Gibbs, Keerthi Kumaran +6
Kitaev's honeycomb model is a paradigmatic exactly solvable system hosting a quantum spin liquid with non-Abelian anyons and topologically protected edge modes, offering a platform…
Convergence guarantee for linearly-constrained combinatorial optimization with a quantum alternating operator ansatz
Brayden Goldstein-Gelb, Phillip C. Lotshaw
We present a quantum alternating operator ansatz (QAOA) that solves a class of linearly constrained optimization problems by evolving a quantum state within a Hilbert subspace…
Ancillary entangling Floquet kicks for accelerating quantum algorithms
C. -C. Joseph Wang, Phillip C. Lotshaw, Titus Morris +3
Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the sys…
Exactly-solved model of light-scattering errors in quantum simulations with metastable trapped-ion qubits
Phillip C. Lotshaw, Brian C. Sawyer, Creston D. Herold +1
We analytically solve a model for light scattering in Ising dynamics of metastable atomic qubits, generalizing the approach of Foss-Feig {\it et al.}~[Phys.~Rev.~A {\bf 87}, 042101…
Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations
Jai Moondra, Phillip C. Lotshaw, Greg Mohler +1
We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut prob…
Performant near-term quantum combinatorial optimization
Titus D. Morris, Ananth Kaushik, Martin Roetteler +1
Combinatorial optimization is a promising application for near-term quantum computers, however, identifying performant algorithms suited to noisy quantum hardware remains as an imp…
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…
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…
On Quantum Entropy and Excess Entropy Production in a System-Environment Pure State
Phillip C. Lotshaw, Michael E. Kellman
We explore a recently introduced quantum thermodynamic entropy of a pure state of a composite system-environment computational "universe" with a simple system $\mathca…
Modelling noise in global Molmer-Sorensen interactions applied to quantum approximate optimization
Phillip C. Lotshaw, Kevin D. Battles, Bryan Gard +3
Many-qubit Mølmer-Sørensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation an…
Simulations of Frustrated Ising Hamiltonians with Quantum Approximate Optimization
Phillip C. Lotshaw, Hanjing Xu, Bilal Khalid +3
Novel magnetic materials are important for future technological advances. Theoretical and numerical calculations of ground state properties are essential in understanding these mat…
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…
Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry
Jerimiah Wright, Meenambika Gowrishankar, Daniel Claudino +4
The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as…
QAOAKit: A Toolkit for Reproducible Study, Application, and Verification of the QAOA
Ruslan Shaydulin, Kunal Marwaha, Jonathan Wurtz +1
Understanding the best known parameters, performance, and systematic behavior of the Quantum Approximate Optimization Algorithm (QAOA) remain open research questions, even as the a…
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…