  Quantum computing offers efficient encapsulation of high-dimensional states.
In this work, we propose a novel quantum reinforcement learning approach that
combines the Advantage Actor-Critic algorithm with variational quantum circuits
by substituting parts of the classical components. This approach addresses
reinforcement learning's scalability concerns while maintaining high
performance. We empirically test multiple quantum Advantage Actor-Critic
configurations with the well known Cart Pole environment to evaluate our
approach in control tasks with continuous state spaces. Our results indicate
that the hybrid strategy of using either a quantum actor or quantum critic with
classical post-processing yields a substantial performance increase compared to
pure classical and pure quantum variants with similar parameter counts. They
further reveal the limits of current quantum approaches due to the hardware
constraints of noisy intermediate-scale quantum computers, suggesting further
research to scale hybrid approaches for larger and more complex control tasks.
