Abstract
The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance on quantum hardware during inference. Hardware imperfections and limited access to quantum devices pose practical challenges. To address this, the Quantum-Train (QT) framework leverages the exponential scaling of quantum amplitudes to generate classical neural network parameters, enabling inference without quantum hardware and achieving significant parameter compression. Yet, designing effective quantum circuit architectures for such quantum-enhanced neural programmers remains non-trivial and often requires expertise in quantum information science. In this paper, we propose an automated solution using differentiable optimization. Our method jointly optimizes both conventional circuit parameters and architectural parameters in an end-to-end manner via automatic differentiation. We evaluate the proposed framework on classification, time-series prediction, and reinforcement learning tasks. Simulation results show that our method matches or outperforms manually designed QNN architectures. This work offers a scalable and automated pathway for designing QNNs that can generate classical neural network parameters across diverse applications.
Abstract (translated)
量子计算（QC）和机器学习（ML）的快速进展催生了量子机器学习（QML），该领域融合了这两个领域的优势。在QML的方法中，变分量子电路（VQCs），也被称为量子神经网络（QNNs），已经在理论上和实证上显示出巨大的潜力。然而，由于推理过程中对量子硬件的依赖，它们的大规模应用受到了限制。量子设备中的硬件缺陷以及获取这些设备的机会有限，给实际操作带来了挑战。为了解决这个问题，Quantum-Train (QT) 框架利用了量子幅度的指数级扩展来生成经典神经网络参数，从而使推理过程中无需使用量子硬件，并实现了显著的参数压缩。然而，设计有效的量子电路架构以支持这种增强型的经典神经程序仍然非常具有挑战性，通常需要量子信息科学的专业知识。 在本文中，我们提出了一种利用可微分优化的自动化解决方案。我们的方法通过自动求导，在端到端的方式下同时优化常规电路参数和架构参数。我们在分类、时间序列预测以及强化学习任务上对提出的框架进行了评估。模拟结果显示，我们的方法与手动设计的QNN架构相当或优于它们。这项工作提供了一种可扩展且自动化的方法来设计能够生成适用于各种应用的经典神经网络参数的量子神经网络（QNN）。
URL
https://arxiv.org/abs/2505.09653
