Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
On the map: Deep reinforcement learning methods → Quantum reinforcement learning
Abstract (arXiv)
Adaptive robot navigation in dynamic environments requires policies that can reach the target reliably while producing efficient and stable trajectories. This paper presents Q-SpiRL, a quantum spiking reinforcement learning framework for obstacle-aware robot navigation. The framework develops and evaluates five agent families: tabular Q-learning, classical MLP, classical SNN, quantum-enhanced MLP (QMLP), and quantum-enhanced spiking neural network (QSNN). While all models are implemented under a unified training and evaluation pipeline, the QSNN is the central architecture of interest, as it combines spike-based temporal processing with variational quantum feature transformation. Experiments are conducted across three grid-world environments of increasing size, namely 20x20, 30x30, and 40x40, with both static and dynamic obstacles. Performance is assessed using success rate, success-weighted path length, path length, and turn rate under deterministic inference. Results show that QSNN achieves the strongest overall trade-off between task completion, trajectory efficiency, and motion smoothness, reaching up to 99% success rate while maintaining high path efficiency in the most challenging setting. Execution on IBM quantum hardware further demonstrates the feasibility of deploying the proposed hybrid policy under real-device conditions.
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 14 of its 42 references that are in the map. First posted on arXiv 20 May 2026.
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- 2021Parametrized quantum policies for reinforcement learningcited by 90
- 2022A Survey on Quantum Reinforcement Learningcited by 53
- 2019A Review of Reinforcement Learning for Autonomous Building Energy Managementcited by 21
- 2022A Comprehensive Review on Autonomous Navigationcited by 19
- 2022Deep Reinforcement Learning with Spiking Q-learningcited by 18
- 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 16
- 2025A Primer on Quantum Machine Learningcited by 12
- 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 9
- 2024LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networkscited by 9
- 2023Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Carscited by 8
- 2024A Quantum Leaky Integrate-and-Fire Spiking Neuron and Networkcited by 5
- 2025The Emergence of Deep Reinforcement Learning for Path Planningcited by 3
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- 2024Research on Autonomous Robots Navigation based on Reinforcement Learning
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