---
title: "分阶段混合：通过知识蒸馏实现视觉量子强化学习"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.30520v1"
permalink: "https://feed.astralor.com/items/itm_cfc5c1c0f4e525e26ef4d541"
published: "2026-06-29T16:28:57.000Z"
score: 56
channel: "ai"
tags: ["变分量子电路","知识蒸馏","视觉量子强化学习","量子机器学习"]
---

# 分阶段混合：通过知识蒸馏实现视觉量子强化学习

## 速览

本研究提出分阶段知识蒸馏策略解决视觉量子强化学习难题。先训练经典视觉教师网络，冻结其编码器作为特征接口，再将策略行为蒸馏至紧凑的下游头（经典或VQC）。在CartPole Pixels和Acrobot Pixels环境中的实验表明，浅层VQC头能习得非平凡视觉控制行为，角度编码VQC头性能接近教师，振幅编码头极度紧凑但更脆弱。该方法将视觉QRL转化为紧凑头学习问题，为在标准端到端循环外训练小型量子兼容策略提供了实用路径。

## 关键点

- 分阶段知识蒸馏避免了端到端训练视觉量子网络的困难，通过先训练经典教师再蒸馏策略到紧凑下游头。
- 冻结的教师编码器作为特征接口，使基于VQC的学生在相同表示下与经典控制公平比较。
- 角度编码VQC头在保持紧凑的同时性能接近教师，而振幅编码头极度紧凑但易受预算和仿真时间影响。

## 全文

Visual environments are a demanding setting for quantum reinforcement learning (QRL): high-dimensional observations, unstable RL optimisation, and constrained variational quantum circuits (VQCs) are difficult to train jointly. This paper studies knowledge distillation (KD) as a staged hybridisation strategy for visual QRL. Instead of training a hybrid visual agent end-to-end from pixels, we first train a classical visual teacher, freeze its encoder as a feature interface, and distil the teacher's policy behaviour into compact downstream heads. These heads can be classical or VQC-based, enabling small quantum-compatible students to be evaluated under the same frozen representation as compact classical controls. We evaluate the pipeline on CartPole Pixels and Acrobot Pixels. The results show that staged KD enables shallow VQC heads to acquire non-trivial visual-control behaviour in settings where direct pixel-based training would be substantially more difficult. Angle-encoded VQC heads retain near-teacher performance, while amplitude-encoded heads push compactness to an extreme regime, at the cost of greater fragility, stronger budget sensitivity, and higher simulation time. Overall, staged KD reframes visual QRL as a compact-head learning problem, opening a practical route for training small quantum-compatible policies outside the standard end-to-end RL loop.

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