Leveraging Adiabatic Quantum Computation for Election Forecasting

  title={Leveraging Adiabatic Quantum Computation for Election Forecasting},
  author={Maxwell P. Henderson and John Novak and Tristan Cook},
Accurate, reliable sampling from fully-connected graphs with arbitrary correlations is a difficult problem. Such sampling requires knowledge of the probabilities of observing every possible state o... 
Breaking limitation of quantum annealer in solving optimization problems under constraints
  • M. Ohzeki
  • Computer Science, Physics
    Scientific Reports
  • 2020
The present study proposes an alternative approach to solve a large-scale optimization problem on the chimera graph via a well-known method in statistical mechanics called the Hubbard-Stratonovich transformation or its variants and can be used to deal with a fully connected Ising model without embedding on the Chimera graph.
Quantum Semantic Learning by Reverse Annealing an Adiabatic Quantum Computer
The feasibility of a complete RBM on AQCs is demonstrated, thanks to an embedding that associates its nodes to virtual qubits, thus outperforming previous implementations based on incomplete graphs and a semantic quantum search which takes the input data as initial boundary conditions to start each learning step of the RBM thanks to a reverse annealing schedule.
Mathematical Methods for a Quantum Annealing Computer
  • R. H. Warren
  • Materials Science
    Journal of Advances in Applied Mathematics
  • 2018
This paper describes the logic and creativity needed in order to have high probability of solving discrete optimization problems on a quantum annealing computer. Current features of quantum computing
Adiabatic computing in the knowledge economy
This article shows that typical for the knowledge economy Optimization problem with Boolean variables may be reduced to the form suitable for solving by the quantum annealing method. That is the
Quantum-Enhanced Grid of the Future: A Primer
The essential elements of quantum computing are discussed and a review of issues concerning this technology are presented to have an across-the-board view of the quantum computing technology applications in power systems, and in particular, in building the grid of the future.
Assessment of image generation by quantum annealer
A discriminator given by a neural network trained on an a priori dataset shows a higher performance of quantum annealer compared with the classical approach for Boltzmann machine learning in training of the generative model, however the generation of the data suffers from the remanent quantum fluctuation in the quantumAnnealer.
Thermal management of a 3D packaging structure for superconducting quantum annealing machines
This work reveals that the heating in such a 3D structure, which seriously reduces the quantum coherence of qubit chips, is effectively prevented by adding through-silicon vias (TSVs).
On Modeling Local Search with Special-Purpose Combinatorial Optimization Hardware
This paper tackles the main challenges of problem size and precision limitation that the Ising hardware model typically suffers from and can be generally used in any local search and refinement solvers that are broadly employed in combinatorial scientific computing algorithms.
Physics-inspired optimization for constraint-satisfaction problems using a digital annealer
This research presents a novel probabilistic method called “1QBit” that combines X-ray crystallography, a high-resolution 3D image analysis, and liquid chromatography to characterize the response of the immune system to carbon dioxide.
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
The results show that the Digital Annealer currently exhibits a time-to-solution speedup of roughly two orders of magnitude for fully connected spin-glass problems with bimodal or Gaussian couplings, over the single-core implementations of simulated annealing and parallel tempering Monte Carlo used in this study.