An Architecture for a Quantum Teleo-Reactive Robot

Area of Science:

• Robotics
• Quantum Computing
• Artificial Intelligence

Background:

• Reactive agents in complex environments face challenges with state classification and action selection under uncertainty.
• Uncertainty stems from sensor noise, perceptual ambiguity, and limited action region separability.

Purpose of the Study:

• To propose a hybrid classical-quantum architecture for reactive agents to address uncertainty.
• To represent and process perceptual states in a quantum feature space for improved decision-making.

Main Methods:

• A hybrid classical-quantum architecture is developed, mapping classical sensor vectors to a quantum feature space.
• Two implementations are explored: a quantum-kernel classifier and a quantum circuit for measurement-based action selection.
• The framework is evaluated using a public wall-following robot dataset as an offline proxy for decision-making.

Main Results:

• The proposed framework effectively represents perceptual ambiguity.
• Quantum-state measurement is successfully connected to the selection of discrete reactive actions.
• The architecture demonstrates potential for enhancing reactive agent capabilities in uncertain environments.

Conclusions:

• The hybrid classical-quantum approach offers a novel method for reactive agents to manage uncertainty.
• This framework shows promise for improving state classification and action selection in robotic systems.
• Further research can explore closed-loop control and quantum advantage demonstrations.