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Efficient classical optimizers are crucial in practical implementations ofVariational Quantum Algorithms (VQAs). In particular, to make StochasticGradient Descent (SGD) resource efficient, adaptive strategies have beenproposed to determine the number of measurement shots used to estimate thegradient. However, existing strategies overlook the overhead that occurs ineach iteration. In terms of wall-clock runtime, significant circuit-switchingand communication latency can slow the optimization process when using a largenumber of iterations. In terms of economic cost when using cloud services,per-task prices can become significant. To address these issues, we present anadaptive strategy that balances the number of shots in each iteration tomaximize expected gain per unit time or cost by explicitly taking into accountthe overhead. Our approach can be applied to not only to the simple SGD butalso its variants, including Adam. Numerical simulations show that our adaptiveshot strategy is actually efficient for Adam, outperforming many existingstate-of-the-art adaptive shot optimizers. However, this is not the case forthe simple SGD. When focusing on the number of shots as the resource, ouradaptive-shots Adam with zero-overhead also outperforms existing optimizers.

Latency-aware adaptive shot allocation for run-time efficient variational quantum algorithms