Rotosolve converges to ε-stationary points for smooth non-convex objectives and ε-suboptimal points under PL, with explicit worst-case rates in the finite-shot regime, outperforming or matching RCD in nuanced ways.
Improving variational quantum circuit optimization via hybrid algorithms and random axis initialization
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A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.
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One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms
Rotosolve converges to ε-stationary points for smooth non-convex objectives and ε-suboptimal points under PL, with explicit worst-case rates in the finite-shot regime, outperforming or matching RCD in nuanced ways.
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Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.