A quantum evidence theory is proposed for uncertainty modeling and reasoning in both closed-world and open-world environments, referred to as QET and GQET, respectively. At the level of uncertainty representation, a series of new concepts are introduced, including (generalized) quantum basic probability amplitude function, (generalized) quantum basic probability distribution, (generalized) quantum belief function, (generalized) quantum plausibility function, and others. At the fusion level, several (generalized) quantum evidential combination rules are proposed to provide a dynamic mechanism for updating and integrating uncertain information from multiple sources, thereby flexibly accommodating diverse application requirements. At the decision-making stage, (generalized) quantum Pignistic transformations are developed to support decision-making processes. In this context, the quantum models of QET and GQET are constructed based on the quantum state representation of the (generalized) quantum basic probability amplitude function, the measurement operators for basis events, the (generalized) quantum basic probability measurements, and the (generalized) belief and plausibility measurements. Quantum evidence theory integrates traditional evidence theory with quantum probability theory, providing a more flexible and powerful framework for uncertainty modeling and reasoning in artificial intelligence. By leveraging the expressive capabilities of quantum state spaces and probability amplitudes, it not only handles incomplete and uncertain information inherent in classical evidence theory but also captures interference effects and non-classical correlations among pieces of information. This enables dynamic information fusion and robust decision-making in complex and uncertain environments.