arXiv:2606.30520v1 Announce Type: cross 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.
The increasing complexity of visual environments for quantum reinforcement learning (QRL) necessitates innovative training methodologies to overcome current limitations.
This research addresses a critical hurdle in making quantum reinforcement learning practical for real-world visual applications, potentially accelerating quantum AI progress.
The proposed 'staged hybridisation' with knowledge distillation provides a more efficient and effective pathway to developing visual QRL agents, bypassing end-to-end training difficulties.
- · Quantum computing researchers
- · AI developers
- · Companies investing in hybrid AI
- · Robotics research
- · Traditional end-to-end QRL approaches
- · Those underestimating quantum AI fusion
It enables more stable and scalable training of quantum reinforcement learning agents for visual tasks.
Improved visual QRL could lead to advanced capabilities in areas like autonomous systems and complex problem-solving.
This method might accelerate the viability of quantum AI in applications previously limited by hardware and training constraints.
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Read at arXiv cs.LG