Quantum annealing for systems of polynomial equations

Numerous scientific and engineering applications require numerically solving systems of equations. Classically solving a general set of polynomial equations requires iterative solvers, while linear equations may be solved either by direct matrix inversion or iteratively with judicious preconditionin...

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Bibliographic Details
Published in:Scientific reports 2019-07, Vol.9 (1), p.10258-9, Article 10258
Main Authors: Chang, Chia Cheng, Gambhir, Arjun, Humble, Travis S, Sota, Shigetoshi
Format: Article
Language:English
Subjects:
Annealing
MATHEMATICS AND COMPUTING
Scaling
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Summary:Numerous scientific and engineering applications require numerically solving systems of equations. Classically solving a general set of polynomial equations requires iterative solvers, while linear equations may be solved either by direct matrix inversion or iteratively with judicious preconditioning. However, the convergence of iterative algorithms is highly variable and depends, in part, on the condition number. We present a direct method for solving general systems of polynomial equations based on quantum annealing, and we validate this method using a system of second-order polynomial equations solved on a commercially available quantum annealer. We then demonstrate applications for linear regression, and discuss in more detail the scaling behavior for general systems of linear equations with respect to problem size, condition number, and search precision. Finally, we define an iterative annealing process and demonstrate its efficacy in solving a linear system to a tolerance of 10 .
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-46729-0