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

We present a novel Adaptive Distribution Generator (ADG) 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(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states toward desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and the generation of structured two-dimensional patterns, 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 Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation.