# Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation

arXiv: https://arxiv.org/abs/2605.20801 · HTML: https://mlsanity.com/paper/2605.20801/

## 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.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 14 of 14)

- [Parametrized quantum policies for reinforcement learning](https://mlsanity.com/paper/2103.05577/) (2021)
- [A Survey on Quantum Reinforcement Learning](https://mlsanity.com/paper/2211.03464/) (2022)
- [A Review of Reinforcement Learning for Autonomous Building Energy Management](https://mlsanity.com/paper/1903.05196/) (2019)
- [A Comprehensive Review on Autonomous Navigation](https://mlsanity.com/paper/2212.12808/) (2022)
- [Deep Reinforcement Learning with Spiking Q-learning](https://mlsanity.com/paper/2201.09754/) (2022)
- [Quantum Deep Reinforcement Learning for Robot Navigation Tasks](https://mlsanity.com/paper/2202.12180/) (2022)
- [A Primer on Quantum Machine Learning](https://mlsanity.com/paper/2511.15969/) (2025)
- [QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction](https://mlsanity.com/paper/2408.03088/) (2024)
- [LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks](https://mlsanity.com/paper/2412.03158/) (2024)
- [Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars](https://mlsanity.com/paper/2311.12875/) (2023)
- [A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network](https://mlsanity.com/paper/2407.16398/) (2024)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems](https://mlsanity.com/paper/2504.20660/) (2025)
- [Current Trends and Advances in Quantum Navigation for Maritime Applications: A Comprehensive Review](https://mlsanity.com/paper/2310.04729/) (2023)

## 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.