Abstract
This paper provides an analysis of a comparison between a quantized quantum neural network and a nonquantized quantum neural network to see if quantization has any possible benefits to quantum neural networks in reinforcement learning. The REINFORCE algorithm, Softmax-Parameterized Quantum Circuits wrapped in a classical encoder, fake quantization, and the CartPole-v1 dataset were also used for these experiments. The findings include a faster convergence to higher mean average reward (from 10 runs) for the quantized quantum neural network, standard deviation and policy entropy steadily decreasing for a longer number of episodes indicating more stable learning, and correlations found between entropy and average rewards and its derivatives. We also found some possible downsides for quantized quantum neural networks, including longer runtimes and less stable evaluation rewards. The experiments were run on Google Colab on the CPU device.