Autonomous Cyber Defence by Quantum-Inspired Deep Reinforcement Learning
Wenbo Feng, Sanyam Vyas, Tingting Li · 2025
With the rapid advancement of computing technologies, the frequency and complexity of cyber-attacks have escalated. Autonomous Cyber Defence (ACD) has emerged to combat these threats, aiming to train defensive agents that can autonomously respond to cyber incidents at machine speed and scale, similar to human defenders. One of the main challenges in ACD is enhancing the training efficiency of defensive agents in complex network environments, typically using Deep Reinforcement Learning (DRL). This work addresses this challenge by employing quantum-inspired methods. When coupled with Quantum-Inspired Experience Replay (QER) buffers and the Quantum Approximate Optimization Algorithm (QAOA), we demonstrate an improvement in training the defence agents against attacking agents in real-world scenarios. While QER and QAOA show great potential for enhancing agent performance, they introduce substantial computational demands and complexity, particularly during the training phase.