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About this paper
Peiyong Wang, Kieran Hymas, James Quach
The paper tests whether entangling gates help a quantum feature extractor learn Pong state relationships within a PPO agent. It compares separable, fixed-CZ, and trainable-IsingZZ circuits with classical MLP backbones. A 56-parameter IsingZZ backbone reached mean final episodic return −5.0, versus −8.7 for a 64-parameter MLP across 10 runs, while a 4,096-parameter MLP reached 15.0.
Key insights
- Entanglement enables PQCs to effectively model interactions between different state variables, enhancing feature extraction beyond simple element-wise nonlinear transformations.
- Optimal performance for entangled PQCs is observed at relatively shallow circuit depths, suggesting a practical 'sweet spot' for trainability and expressivity, potentially related to the barren plateau phenomenon.
- Entangled quantum backbones learn representations that are fundamentally different from those generated by classical neural networks, indicating they explore distinct regions of the representation space.
Summary by alphaXiv.
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