Quantum Computing Applied to Robot Swarm Navigation

We investigate quantum computing methods for coordinated robot swarm navigation, with a focus on mini-AUVs. Our research examines how local decisions and interactions generate collective movement, target search, and exploration. We explore quantum circuits and density-matrix models within hybrid quantum–classical algorithms for swarm coordination. Additionally, we study how uncertainty, limited communication, and changing network connections influence navigation and collective behavior. Through simulations and comparisons with classical methods, we aim to identify practical benefits and limitations of quantum approaches.

Quantum Reservoir Computing

Quantum reservoir computing (QRC) uses the evolving states of a quantum system to recognize patterns in data that change over time. By retaining traces of past inputs, the reservoir provides a form of memory for learning from complex signals.

We investigate QRC for classifying electroencephalogram (EEG) recordings in studies of epilepsy and mild traumatic brain injury. We also explore how QRC can help explain and predict the behavior of agent swarms, such as groups of autonomous robots. A central question is how local interactions between individual agents give rise to coordinated patterns across the group, a process known as emergence.