# Optimizing Embedding-Related Quantum Annealing Parameters for Reducing Hardware Bias

@article{Barbosa2020OptimizingEQ, title={Optimizing Embedding-Related Quantum Annealing Parameters for Reducing Hardware Bias}, author={Aaron Barbosa and Elijah Pelofske and Georg Hahn and Hristo N. Djidjev}, journal={ArXiv}, year={2020}, volume={abs/2011.00719} }

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 Systems, Inc., is limited by environmental noise and hardware biases. One way to deal with these imperfections and to improve the quality of the annealing results is to apply a variety of pre-processing techniques such as spin reversal (SR), anneal offsets (AO), or chain weights (CW). Maximizing the effectiveness of these…

## 3 Citations

Reducing quantum annealing biases for solving the graph partitioning problem

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This work quantifies the bias of the implementation of the constraint on the quantum annealer and proposes an iterative method to correct any biases, and applies this concept to Graph Partitioning, an important NP-hard problem, which asks to find a partition of the vertices of a graph that is balanced and minimizes the cut size.

Using Machine Learning for Quantum Annealing Accuracy Prediction

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This work focuses on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry, and trains a machine learning regression model that predicts the clique size found by D-Wave.

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This work proposes a novel method, called parallel quantum annealer, to make better use of available qubits, wherein either the same or several independent problems are solved in the same annealing cycle of a quantum anNealer, assuming enough physical qubits are available to embed more than one problem.

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