Quantum State Representation with Dynamic Kernel Expansion

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Abstract: Quantum state representation is a fundamental concept in quantum information theory, crucial for quantum algorithms and quantum computation. Traditional state representations often rely on fixed dimensionality, limiting the expressiveness of quantum states. This paper introduces a novel quantum state representation that dynamically expands the kernel of a state space, allowing for more expressive representations. We propose a neural network-based approach to learn a dynamic kernel function, enabling a flexible and powerful representation of quantum states. This advancement offers a significant step towards more efficient quantum algorithms and a deeper understanding of quantum phenomena. The proposed method demonstrates improved performance in various quantum state comparison and manipulation tasks.

DOI: 10.5281/zenodo.22290270

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