# Practical integer-to-binary mapping for quantum annealers

@article{Karimi2019PracticalIM, title={Practical integer-to-binary mapping for quantum annealers}, author={Sahar Karimi and Pooya Ronagh}, journal={Quantum Information Processing}, year={2019}, volume={18}, pages={1-24} }

Recent advancements in quantum annealing hardware and numerous studies in this area suggest that quantum annealers have the potential to be effective in solving unconstrained binary quadratic programming problems. Naturally, one may desire to expand the application domain of these machines to problems with general discrete variables. In this paper, we explore the possibility of employing quantum annealers to solve unconstrained quadratic programming problems over a bounded integer domain. We…

## 6 Citations

Performance Comparison of Typical Binary-Integer Encodings in an Ising Machine

- Computer ScienceIEEE Access
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The differences in performance among binary-integer encodings in an Ising machine, which can solve combinatorial optimization problems, are investigated and unary encoding shows the best performance for large-sized problems.

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Noisy Intermediate-Scale Quantum (NISQ) algorithms require novel paradigms of error mitigation. To obtain noise-robust quantum computers, each logical qubit is equipped with hundreds or thousands of…

Quantum Annealing for Industry Applications: Introduction and Review

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Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the…

Calculating Nash equilibrium and Nash bargaining solution on quantum annealers

- Computer Science, PhysicsArXiv
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It is shown that adding penalty terms to the quadratic function formulation of Nash equilibrium, and reducing the higher powers of the polynomial expression for the Nash bargaining problem, together with addition of penalty terms, gives QUBO formulation of these problems for execution on quantum annealers.

Least-squares solutions to polynomial systems of equations with quantum annealing

- Computer Science, MathematicsQuantum Inf. Process.
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The proposed methodology maps the squared-error function for a polynomial equation onto the Ising–Hamiltonian model, ensuring that the approximate solutions to real-world problems can be computed on a quantum annealer even when the exact solutions do not exist.

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