Optimizing Embedding-Related Quantum Annealing Parameters for Reducing Hardware Bias

  title={Optimizing Embedding-Related Quantum Annealing Parameters for Reducing Hardware Bias},
  author={Aaron Barbosa and Elijah Pelofske and Georg Hahn and Hristo N. Djidjev},
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… 
Reducing quantum annealing biases for solving the graph partitioning problem
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
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.
Parallel quantum annealing
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.


Solving Large Maximum Clique Problems on a Quantum Annealer
This article investigates methods for decomposing larger problem instances into smaller ones, which can subsequently be solved on D-Wave, and prune as many generated subproblems that don’t contribute to the solution as possible in order to reduce the computational complexity.
Inferring the Dynamics of Ground-State Evolution of Quantum Annealers
This work proposes to use advanced features of the newest annealer generation, the D-Wave 2000Q, to indirectly infer information about the dynamics of the ground-state during the anneal process, and introduces an approximate technique to determine the freeze-out point of the system as well as of individual qubits.
Finding Maximum Cliques on the D-Wave Quantum Annealer
It is demonstrated that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms, and for instances specifically designed to fit well the DW qubit interconnection network, substantial speed-ups in computing time over classical approaches are observed.
Degeneracy, degree, and heavy tails in quantum annealing
Both simulated quantum annealing and physical quantum annealing have shown the emergence of "heavy tails" in their performance as optimizers: The total time needed to solve a set of random input
Parameter setting for quantum annealers
  • K. Pudenz
  • Mathematics, Computer Science
    2016 IEEE High Performance Extreme Computing Conference (HPEC)
  • 2016
Several strategies for setting physical parameters on quantum annealers for application problems that do not fit natively on the hardware graph are developed and applied, yielding results that generalize to guidelines regarding which parameter setting strategies to use for different classes of problems.
Optimizing the Spin Reversal Transform on the D-Wave 2000Q
This work investigates the effectiveness of the spin reversal transform for D-Wave 2000Q, and considers two important NP-hard problems, the Maximum Clique and the Minimum Vertex Cover problems, and shows on a variety of input problem graphs that using thespin reversal transform can yield substantial improvements in solution quality.
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
It is demonstrated that the new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous spacefunctions Converges faster and with more certainty than manyother acclaimed global optimization methods.
Mulbregt, and SciPy 1.0 Contributors
  • Nature Methods,
  • 2020
System Documentation: Solving a Problem on the QPU -Using Spin-Reversal (Gauge) Transforms
  • 2020
Boosting integer factoring performance via quantum annealing offsets
  • Technical report, D-Wave Systems,
  • 2016