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

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.

@article{chang2025_2504.13532,
  title={ Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration },
  author={ Yen-Jui Chang and Wei-Ting Wang and Chen-Yu Liu and Yun-Yuan Wang and Ching-Ray Chang },
  journal={arXiv preprint arXiv:2504.13532},
  year={ 2025 }
}