Quantum Team Awarded 2020 Best Paper Award in Algorithms Journal
Columbia MD-- June 3, 2021. The Algorithms Award Committee recently announced the winner of the 2020 Best Paper Award. The award was given for a quantum research article written by a team of authors: Stuart Hadfield, Ph.D. (USRA), Zhihui Wang, Ph.D. (USRA), Bryan O’Gorman, Ph.D. (NASA), Eleanor G. Rieffel, Ph.D., (NASA), Davide Venturelli, Ph.D. (USRA) and Rupak Biswas, Ph.D. (NASA).
All original research articles published in the open access journal, Algorithms, between January 1 and December 31, 2019 were eligible for the award. These articles were thoroughly evaluated by the award committee and the award was based on novelty of the research objectives presented and the download/citation rates in 2020. The authors will now have the opportunity to publish a paper in 2021, courtesy of Algorithms journal. Prof. Frank Werner, Ph.D., the Editor-in-Chief of Algorithms, made the announcement regarding the award on behalf of the Assessment Committee, congratulating the winners on the accomplishment.
The full citation of the research paper is as follows:
Title: From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
Authors: Stuart Hadfield, Zhihui Wang, Bryan O’Gorman, Eleanor G. Rieffel, Davide Venturelli and Rupak Biswas
Volume 12, Issue 2, doi: 10.3390/a12020034
Additional Resources : https://www.mdpi.com/journal/algorithms/awards/984
Absrtract : Emerging quantum gate-model devices will enable implementation of a wider variety of algorithms. Of particular interest are heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications. A leading candidate is the quantum approximate optimization algorithm, which alternates between application of cost and mixing Hamiltonians. We extend this approach to the quantum alternating operator ansatz, with alternation now between more general parameterized families of unitaries. For cases requiring mixing within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficient quantum circuit constructions. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace. More efficient implementation will enable earlier exploration of quantum approaches to a wide variety of approximate optimization, exact optimization, and sampling problems, and we detail circuit mappings for a diverse set of problems.