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Quantum gradient descent and Newton's method for constrained polynomial optimization
2019
New Journal of Physics
Optimization problems in disciplines such as machine learning are commonly solved with iterative methods. Gradient descent algorithms find local minima by moving along the direction of steepest descent while Newton's method takes into account curvature information and thereby often improves convergence. Here, we develop quantum versions of these iterative optimization algorithms and apply them to polynomial optimization with a unit norm constraint. In each step, multiple copies of the current
doi:10.1088/1367-2630/ab2a9e
fatcat:l6zmjcjgnjgy7bowjwg6pgdctm