Modern robotic systems increasingly rely on learning-based perception, prediction, and decision making modules that must operate under strict constraints on latency, compute, energy, and reliability. In many robotic platforms (mobile robots, drones, autonomous vehicles, ...), inference must execute on embedded or edge hardware while remaining robust to sensor noise, distribution shifts, and safety-critical failures. Robotics literature emphasizes that autonomy pipelines integrate learned components with planning and control, and that practical deployment requires careful attention to computational limits and robustness in real environments
Quantum computers are increasingly accessible for experimentation, yet they remain difficult to deploy directly onboard robots due to hardware availability, latency, sampling cost, and noise. Previous work on quantum machine learning (QML) emphasize that these constraints shape what is feasible with noisy intermediate-scale quantum devices and motivate hybrid workflows that use quantum computation selectively during training rather than at deployment.
At the same time, QML models (such as quantum kernel methods and variational quantum classifiers) aim to exploit quantum feature spaces and parameterized circuits to represent decision functions that may be difficult to capture with compact classical models. However, even when a quantum model shows promise, running it as a production inference engine is often impractical in robotic systems. Knowledge distillation provides a principled way to transfer predictive behaviour from a
teacher model to a smaller student model by training the student to mimic the teacher’s outputs (often using soft probability targets), enabling efficient deployment. This proposal investigates quantum-to-classical distillation for robotics: a quantum teacher generates supervision signals during training, while the deployed model remains a standard classical student suitable for real-time inference.
Interested candidates must send their application to Ezio Malis at ezio.malis@inria.fr. More information can the found on the web pages
https://3ia.univ-cotedazur.eu/about/apply/call-for-applications-phd-post...
