238 citations · 608 across the 18 of their papers we have counts for
18 papers
ADAPT-QAOA with a classically inspired initial state
Vishvesha K. Sridhar, Yanzhu Chen, Bryan Gard +2
Quantum computing may provide advantage in solving classical optimization problems. One promising algorithm is the quantum approximate optimization algorithm (QAOA). There have bee…
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…
Warm-Started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson's Max-Cut at Low Circuit Depths
Reuben Tate, Jai Moondra, Bryan Gard +2
We generalize the Quantum Approximate Optimization Algorithm (QAOA) of Farhi et al. (2014) to allow for arbitrary separable initial states with corresponding mixers such that the s…
A Classically Efficient Quantum Scalable Fermi-Hubbard Benchmark
Bryan T. Gard, Adam M. Meier
In order to quantify the relative performance of different testbed quantum computing devices, it is useful to benchmark them using a common protocol. While some benchmarks rely on…
Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms
Kübra Yeter-Aydeniz, Bryan T. Gard, Jacek Jakowski +5
The rapid progress of noisy intermediate-scale quantum (NISQ) computing underscores the need to test and evaluate new devices and applications. Quantum chemistry is a key applicati…
Gate-free state preparation for fast variational quantum eigensolver simulations: ctrl-VQE
Oinam Romesh Meitei, Bryan T. Gard, George S. Barron +4
The variational quantum eigensolver (VQE) is currently the flagship algorithm for solving electronic structure problems on near-term quantum computers. This hybrid quantum/classica…