分阶段混合：通过知识蒸馏实现视觉量子强化学习

Quantum Physics

arXiv:2606.30520v1 (quant-ph) [Submitted on 29 Jun 2026]

Title:Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation

Authors:Javier Lazaro, Juan-Ignacio Vazquez, Pablo Garcia-Bringas View a PDF of the paper titled Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation, by Javier Lazaro and 2 other authors View PDF HTML (experimental)

Abstract: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.

Subjects:	Quantum Physics (quant-ph); Machine Learning (cs.LG)
Cite as:	arXiv:2606.30520 [quant-ph]
	(or arXiv:2606.30520v1 [quant-ph] for this version)
	https://doi.org/10.48550/arXiv.2606.30520 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Javier Lazaro [view email]
[v1] Mon, 29 Jun 2026 16:28:57 UTC (641 KB)
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