Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillation

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)
Full-text links:

Access Paper:

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)
• TeX Source

view license

Current browse context:

quant-ph < prev   |   next >
new | recent | 2026-06 Change to browse by: cs
cs.LG

References & Citations

• INSPIRE HEP
  
• NASA ADS
• Google Scholar
• Semantic Scholar

export BibTeX citation Loading...

BibTeX formatted citation

× Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos

Demos

Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers

Recommenders and Search Tools

Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?)

• Author
• Venue
• Institution
• Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)