Hybrid Quantum–Classical Optimization of Drone Activation Using Grover’s Algorithm and Neural Networks

Abstract

This paper presents a didactic proof-of-concept for a hybrid quantum–classical framework for optimizing drone activation and coordination using Grover’s quantum search algorithm combined with a neural network trained on quantum-sampled data. The proposed approach models the activation of each drone as a qubit, encoding feasible mission configurations in a high-dimensional binary state space. A Grover-based oracle is constructed to mark energetically favorable configurations that balance mission coverage, battery constraints, and inter-drone interference. Due to current simulation constraints, the problem is evaluated at a small scale ( $n=10$ ) to focus on the pipeline’s mechanics rather than demonstrating immediate quantum supremacy. The resulting quantum circuit, implemented in Cirq, is simulated to obtain probabilistic samples of low-energy states, which are then used to train a feed-forward neural network. The trained model successfully generalizes the energy landscape and predicts optimal configurations without exhaustive enumeration. Numerical experiments confirm that quantum-sampled training data enhance neural prediction accuracy and efficiently approximate the global optimum found by classical brute-force search. This study demonstrates how Grover’s algorithm can serve as a quantum data generator to improve classical learning models, serving as an exploratory step toward future, larger-scale implementations.