Differentiable Optimization of Quantum Circuits

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel approach to optimizing quantum circuits by directly calculating and applying gradients. Traditional quantum algorithm optimization relies heavily on simulating the quantum circuit on classical computers, which is computationally expensive and limits the scalability of these algorithms. Our work proposes a framework for differentiable quantum circuit optimization, treating quantum circuits as differentiable functions and employing gradient-based optimization techniques. We outline a methodology for computing gradients of the expectation values of quantum circuits with respect to their adjustable parameters. This allows for the direct optimization of quantum circuits using algorithms such as gradient descent, potentially leading to significant improvements in the efficiency and accuracy of quantum algorithms. The core contribution lies in providing a mathematically rigorous foundation for this differentiable approach, paving the way for automated quantum algorithm design and enhancement. The presented method offers a viable alternative to classical simulation, addressing a key bottleneck in the practical implementation of quantum algorithms.

DOI: 10.5281/zenodo.22270057

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