Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration
This work addresses the challenge of efficient probability distribution generation for applications such as financial modeling and pattern recognition, representing an incremental advancement in bridging quantum algorithms with practical high-performance computing.
The paper tackles the problem of generating target probability distributions by developing a quantum walks-based adaptive distribution generator that integrates variational quantum circuits with discrete-time quantum walks, achieving high simulation fidelity for applications like financial simulation and digit pattern generation. The method is implemented with CUDA-Q GPU acceleration to reduce computational overhead and improve scalability compared to conventional approaches.
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.