  Financial market prediction and optimal trading strategy development remain
challenging due to market complexity and volatility. Our research in quantum
finance and reinforcement learning for decision-making demonstrates the
approach of quantum-classical hybrid algorithms to tackling real-world
financial challenges. In this respect, we corroborate the concept with rigorous
backtesting and validate the framework's performance under realistic market
conditions, by including fixed transaction cost per trade. This paper
introduces a Quantum Attention Deep Q-Network (QADQN) approach to address these
challenges through quantum-enhanced reinforcement learning. Our QADQN
architecture uses a variational quantum circuit inside a traditional deep
Q-learning framework to take advantage of possible quantum advantages in
decision-making. We gauge the QADQN agent's performance on historical data from
major market indices, including the S&P 500. We evaluate the agent's learning
process by examining its reward accumulation and the effectiveness of its
experience replay mechanism. Our empirical results demonstrate the QADQN's
superior performance, achieving better risk-adjusted returns with Sortino
ratios of 1.28 and 1.19 for non-overlapping and overlapping test periods
respectively, indicating effective downside risk management.
