mlsanityData to 14 Oct 2026

Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems

arXiv 2504.20660 ↗First posted 29 Apr 2025cs.LG cs.ET cs.ITcited by 2

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

Abstract (arXiv)

In this paper, a novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning. By leveraging the inherent parallelism of quantum computing, the proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline. The Classical Quantum fusion results in rapid convergence of training, reducing the training time significantly and improved adaptability in scenarios featuring static, dynamic, and moving obstacles. Simulator based evaluations demonstrate significant enhancements in path efficiency, trajectory smoothness, and mission success rates, underscoring the potential of framework for real time, autonomous navigation in complex and unpredictable environments. Furthermore, the proposed framework was tested beyond simulations on practical scenarios, including real world map data such as the IIT Delhi campus, reinforcing its potential for real time, autonomous navigation in complex and unpredictable environments.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 26 references that are in the map. First posted on arXiv 29 Apr 2025.

Builds on

6 of 26 references.

References · 6

  1. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  2. 2017Traffic flow optimization using a quantum annealercited by 29
  3. 2020Quantum reinforcement learning in continuous action spacecited by 29
  4. 2014Theta*: Any-Angle Path Planning on Gridscited by 26
  5. 2016Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networkscited by 2
  6. 2024Deep-Sea A*+: An Advanced Path Planning Method Integrating Enhanced A* and Dynamic Window Approach for Autonomous Underwater Vehiclescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposalcited by 0
  2. 2026Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
  2. 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars
  3. 2025Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
  4. 2026Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
  5. 2025Lyapunov-Aware Quantum-Inspired Reinforcement Learning for Continuous-Time Vehicle Control: A Feasibility Study
  6. 2025Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments
  7. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components
  8. 2025Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization