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- W4320342624 abstract "Efficient classical optimizers are crucial in practical implementations of Variational Quantum Algorithms (VQAs). In particular, to make Stochastic Gradient Descent (SGD) resource efficient, adaptive strategies have been proposed to determine the number of measurement shots used to estimate the gradient. However, existing strategies overlook the overhead that occurs in each iteration. In terms of wall-clock runtime, significant circuit-switching and communication latency can slow the optimization process when using a large number of iterations. In terms of economic cost when using cloud services, per-task prices can become significant. To address these issues, we present an adaptive strategy that balances the number of shots in each iteration to maximize expected gain per unit time or cost by explicitly taking into account the overhead. Our approach can be applied to not only to the simple SGD but also its variants, including Adam. Numerical simulations show that our adaptive shot strategy is actually efficient for Adam, outperforming many existing state-of-the-art adaptive shot optimizers. However, this is not the case for the simple SGD. When focusing on the number of shots as the resource, our adaptive-shots Adam with zero-overhead also outperforms existing optimizers." @default.
- W4320342624 created "2023-02-13" @default.
- W4320342624 creator A5078169098 @default.
- W4320342624 date "2023-02-08" @default.
- W4320342624 modified "2023-09-23" @default.
- W4320342624 title "Latency-aware adaptive shot allocation for run-time efficient variational quantum algorithms" @default.
- W4320342624 doi "https://doi.org/10.48550/arxiv.2302.04422" @default.
- W4320342624 hasPublicationYear "2023" @default.
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