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

arXiv: https://arxiv.org/abs/2504.13532 · HTML: https://mlsanity.com/paper/2504.13532/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science](https://mlsanity.com/paper/2308.01999/) (2023)
- [Learning hard quantum distributions with variational autoencoders](https://mlsanity.com/paper/1710.00725/) (2017)
- [Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems](https://mlsanity.com/paper/2304.10159/) (2023)

## Abstract (arXiv)

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.