arXiv：cs.LG· Rui Che, Ludvig af Klinteberg·· 3 小时前
用 QNN 实现流模型量子电路的高效执行
An Efficient Quantum Circuit for Flow Model Execution Using Quantum Neural Networks
AI 导读
研究者提出一种基于量子神经网络（QNN）的量子电路方案，用于在量子计算机上高效执行流模型。该方法在 QROM 相位回弹框架基础上，改用训练好的 QNN 替代 QROM 进行数据编码，在保持波函数流模拟精度的同时显著降低了电路资源消耗。
正文
Abstract:Flow models generate trajectories from an initial distribution to a target distribution by solving an ordinary differential equation defined by a velocity field. Flow matching learns this velocity field by modeling the transport dynamics between the two distributions. Wavefunction flow establishes a formal connection between flow models and quantum dynamics by introducing a continuity Hamiltonian, which drives the Schrödinger evolution of quantum states. In this paper, we investigate accurate and efficient quantum simulation of the wavefunction flow, thereby realizing the efficient implementation of flow models on quantum computers. We first leverage a quantum read-only memory (QROM)-based phase kickback framework for the wavefunction flow simulation, generating probability densities that closely match those produced by the corresponding conventional flow model. To address the high circuit-resource cost, we further incorporate a trained quantum neural network (QNN) into the phase kickback framework, replacing QROM for data encoding. Numerical experiments demonstrate that our proposed method implements flow models on quantum computers more efficiently, since it maintains the accuracy of wavefunction flow simulation compared with the QROM-based framework, and significantly reduces the circuit resources.
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11537 [quant-ph] |
| (or arXiv:2610.11537v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11537 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ludvig Af Klinteberg [view email]
[v1]
Thu, 8 Oct 2026 09:07:01 UTC (5,618 KB)
来源：arXiv：cs.LG · arxiv.org