This paper focuses on quantum-enhanced edge computing for resource allocation optimization in heterogeneous Internet of Things (IoT) networks, proposing a deep reinforcement learning (DRL)-based algorithm. Traditional resource management approaches often face challenges in scalability, latency, and power consumption as IoT deployments expand. To address these limitations, our quantum-enhanced DRL framework leverages the accelerated learning and improved decision-making capabilities of quantum computing. Simulation results show that quantum-enhanced algorithms outperform classical methods, offering faster convergence, higher throughput, and greater tolerance to quantum noise. This study demonstrates that integrating quantum computing with edge computing can significantly enhance resource management in IoT systems, enabling smarter and more energy-efficient network operations.