# Optimization on black-box function by parameter-shift rule

arXiv: https://arxiv.org/abs/2503.13545 · HTML: https://mlsanity.com/paper/2503.13545/

## Key facts

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- First posted on arXiv 16 Mar 2025.

## Abstract (arXiv)

Machine learning has been widely applied in many aspects, but training a machine learning model is increasingly difficult. There are more optimization problems named "black-box" where the relationship between model parameters and outcomes is uncertain or complex to trace. Currently, optimizing black-box models that need a large number of query observations and parameters becomes difficult. To overcome the drawbacks of the existing algorithms, in this study, we propose a zeroth-order method that originally came from quantum computing called the parameter-shift rule, which has used a lesser number of parameters than previous methods.