Quantum Circuit Structure Optimization for Quantum Reinforcement Learning
This work addresses a bottleneck in quantum reinforcement learning for researchers in quantum computing and AI, though it is incremental as it builds on existing QRL methods.
The paper tackles the problem of inefficient quantum reinforcement learning (QRL) due to fixed parameterized quantum circuit (PQC) structures by proposing a QRL-NAS algorithm that optimizes these structures, resulting in higher rewards compared to fixed circuits.
Reinforcement learning (RL) enables agents to learn optimal policies through environmental interaction. However, RL suffers from reduced learning efficiency due to the curse of dimensionality in high-dimensional spaces. Quantum reinforcement learning (QRL) addresses this issue by leveraging superposition and entanglement in quantum computing, allowing efficient handling of high-dimensional problems with fewer resources. QRL combines quantum neural networks (QNNs) with RL, where the parameterized quantum circuit (PQC) acts as the core computational module. The PQC performs linear and nonlinear transformations through gate operations, similar to hidden layers in classical neural networks. Previous QRL studies, however, have used fixed PQC structures based on empirical intuition without verifying their optimality. This paper proposes a QRL-NAS algorithm that integrates quantum neural architecture search (QNAS) to optimize PQC structures within QRL. Experiments demonstrate that QRL-NAS achieves higher rewards than QRL with fixed circuits, validating its effectiveness and practical utility.