Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

• URL: http://arxiv.org/abs/2504.13532v1
• Date: Fri, 18 Apr 2025 07:53:03 GMT
• Title: Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration
• Authors: Yen-Jui Chang, Wei-Ting Wang, Chen-Yu Liu, Yun-Yuan Wang, Ching-Ray Chang,
• Abstract summary: We present a novel Adaptive Distribution Generator that leverages a quantum walks-based approach to generate high precision and efficiency of target probability distributions.<n>Our method integrates variational quantum circuits with discrete-time quantum walks, specifically, split-step quantum walks and their entangled extensions, to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions.
• Score: 0.5679775668038153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel Adaptive Distribution Generator that leverages a quantum walks-based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks, specifically, split-step quantum walks and their entangled extensions, to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations(0~9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walks-Based Adaptive Distribution Generator achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation.

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