Quantum Reinforcement Learning

Reinforcement learning (RL) underpins many modern decision-making systems, from robotics and resource scheduling to adaptive control and autonomous agents. However, as problem complexity grows, classical RL can become bottlenecked by high-dimensional state spaces, expensive policy evaluation, and slow exploration in large action spaces. This project investigates Quantum Reinforcement Learning (QRL) as a principled route to accelerate or enhance RL pipelines by leveraging quantum representations and quantum-assisted optimization, while remaining compatible with near-term (NISQ) hardware through hybrid quantum–classical training loops.

We will develop and benchmark quantum-enhanced RL agents that integrate parameterized quantum circuits (PQCs) as policy and/or value-function approximators, and explore quantum subroutines for components such as feature embedding, policy search, and exploration. The work will emphasize theoretical soundness, clearly specifying what is quantum, what remains classical, and how learning signals (returns, advantages, gradients) are estimated under realistic constraints such as finite sampling (“shots”) and device noise. We will evaluate performance using reproducible benchmarks and establish practical guidelines for when quantum components can provide measurable benefits (e.g., sample efficiency, expressivity under parameter constraints, robustness, or computational advantages in specific regimes).

The project aims to deliver (i) a coherent QRL framework suitable for real devices, (ii) open, reusable benchmarking code and protocols, and (iii) a set of well-characterized use cases, such as control, scheduling, and resource allocation, where QRL is most likely to offer value for next-generation intelligent systems.