aGPSR estimates quantum derivatives with K shifted evaluations instead of the exponential number required by exact generalized parameter shift rules, at the cost of a controllable approximation error.
Noise-robust optimization of quantum machine learning models for polymer properties using a simulator and validated on the ionq quantum computer
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Evaluation of derivatives using approximate generalized parameter shift rule
aGPSR estimates quantum derivatives with K shifted evaluations instead of the exponential number required by exact generalized parameter shift rules, at the cost of a controllable approximation error.