Quantum RL Matches Classical Deep RL with 100x Fewer Parameters

According to the paper "Quantum RL vs. Classical Deep RL: A New Era for Dynamic Portfolio Optimization?" by Vincent Gurgul, Ying Chen, and Stefan Lessmann, Quantum Reinforcement Learning agents using Variational Quantum Circuits achieve performance comparable to state-of-the-art classical models like DDPG and DQN - but with orders of magnitude fewer trainable parameters. This represents a potential paradigm shift in parameter-efficient AI for financial applications.