用量子行走自适应生成高精度概率分布，支持金融模拟与数字图像生成。
Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration
- 结合变分量子电路与分步量子行走，动态调节参数生成目标分布。
- 在0~9数字模式生成任务中实现高保真度，计算效率显著优于传统方法。
- 基于CUDA-Q框架实现GPU加速，适合需高效量子模拟的工程应用。
我们提出一种新型自适应概率分布生成器，采用基于量子行走的方法，实现目标概率分布的高精度与高效率生成。该方法将变分量子电路与离散时间量子行走（特别是分步量子行走及其纠缠扩展）相结合，通过动态调节量子比特的“硬币”参数，引导量子态演化至期望分布。该方法可精确建模一维概率分布，适用于金融模拟；也可生成结构化的二维模式，如数字0~9的表示。在CUDA-Q框架下实现，利用GPU加速显著降低计算开销，提升可扩展性。大量基准测试表明，本方法具有高仿真保真度，弥合了理论量子算法与实际高性能计算之间的差距。
原文摘要 · Abstract (English)
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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