136 citations · 513 across the 37 of their papers we have counts for
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Optimizing embedding-related quantum annealing parameters for reducing hardware bias
Aaron Barbosa, Elijah Pelofske, Georg Hahn +1
Quantum annealers have been designed to propose near-optimal solutions to NP-hard optimization problems. However, the accuracy of current annealers such as the ones of D-Wave Syste…
Advanced anneal paths for improved quantum annealing
Elijah Pelofske, Georg Hahn, Hristo Djidjev
Advances in quantum annealing technology make it possible to obtain high quality approximate solutions of important NP-hard problems. With the newer generations of the D-Wave annea…
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking
Nasrin Akhter, Gopinath Chennupati, Hristo Djidjev +1
Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of…
Decomposition algorithms for solving NP-hard problems on a quantum annealer
Elijah Pelofske, Georg Hahn, Hristo Djidjev
NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp's 21 NP-hard problems, have several applications in computational chemistry, biochemistry…
Inferring the Dynamics of the State Evolution During Quantum Annealing
Elijah Pelofske, Georg Hahn, Hristo Djidjev
To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUB…
Advanced unembedding techniques for quantum annealers
Elijah Pelofske, Georg Hahn, Hristo Djidjev
The D-Wave quantum annealers make it possible to obtain high quality solutions of NP-hard problems by mapping a problem in a QUBO (quadratic unconstrained binary optimization) or I…