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The rapid evolution of financial markets necessitates computational approaches that can process vast datasets with low latency. While Deep Reinforcement Learning (DRL) has shown promise in algorithmic trading, it often suffers from instability and slow convergence in volatile environments. Simultaneously, the emergence of Quantum Computing offers potential speedups for optimization problems. This paper proposes a Hybrid Quantum-Classical Neural Network (HQC-NN) framework for portfolio management. We integrate a parameterized quantum circuit (PQC) into a classical reinforcement learning agent to enhance feature representation and policy optimization. Experimental results on the S&P 500 high-frequency data demonstrate that the HQC-NN model outperforms classical Long Short-Term Memory (LSTM) networks and standard Deep Q-Networks (DQN) in terms of cumulative return and Sharpe ratio, while maintaining computational feasibility on near-term quantum simulators. ifast22
The year was 2022, but the effects are still rippling through every dashboard, trade ticket, and client interaction we handle today. Disclaimer: This article is for informational purposes only
The HQC-NN achieved the highest cumulative return and Sharpe ratio. Notably, the Maximum Drawdown is significantly lower than that of classical models. We attribute this to the VQC's ability to capture non-linear correlations between assets that classical LSTMs might miss. The quantum feature space appears to provide richer representations during high-volatility periods, allowing the agent to hedge more effectively. The year was 2022