mlsanityData to 14 Oct 2026

Quantum Circuit Structure Optimization for Quantum Reinforcement Learning

arXiv 2507.00589 ↗First posted 1 Jul 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 16 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

6 of 16 references.

References · 6

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  4. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
  5. 2023Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperationcited by 6
  6. 2021Trends in Neural Architecture Search: Towards the Acceleration of Searchcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components
  2. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
  3. 2024Reinforcement learning-based architecture search for quantum machine learning
  4. 2024Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization
  5. 2025Quantum Architecture Search for Solving Quantum Machine Learning Tasks
  6. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
  7. 2026Modeling quantum neural network gradient with reinforcement learning
  8. 2024An Introduction to Quantum Reinforcement Learning (QRL)