# Path Planning for Cellular-Connected UAV: A DRL Solution with Quantum-Inspired Experience Replay

arXiv: https://arxiv.org/abs/2108.13184 · HTML: https://mlsanity.com/paper/2108.13184/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy](https://mlsanity.com/paper/2508.00768/) (2025)
- [Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missions](https://mlsanity.com/paper/2512.04773/) (2025)
- [Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks](https://mlsanity.com/paper/2606.25480/) (2026)

## Abstract (arXiv)

In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage of UAV's adjustable mobility, an intelligent UAV navigation approach is formulated to achieve the aforementioned optimization goal. Specifically, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL) solution with novel quantum-inspired experience replay (QiER) framework is proposed to help the UAV find the optimal flying direction within each time slot, and thus the designed trajectory towards the destination can be generated. Via relating experienced transition's importance to its associated quantum bit (qubit) and applying Grover iteration based amplitude amplification technique, the proposed DRL-QiER solution commits a better trade-off between sampling priority and diversity. Compared to several representative baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results.