Recent advances in quantum computing have opened new possibilities for modeling high-dimensional and entangled structures in musical data. This paper introduces a Quantum Variational Autoencoder (QVAE) architecture for symbolic music generation, leveraging quantum latent spaces to capture superposed and correlated melodic patterns beyond the expressive capacity of classical generative models. By encoding MIDI-based musical sequences into parameterized quantum circuits, the proposed framework enabled the exploration of superposed latent states, facilitating multiple potential melodic continuations within a unified probabilistic representation. The study evaluated QVAE against conventional Variational Autoencoders using both objective metrics, including latent diversity, reconstruction accuracy, and sequence entropy, and subjective listening tests assessing musical coherence and creativity. Experimental results demonstrated that QVAE achieved enhanced diversity in generated melodies while maintaining stylistic consistency, suggesting that quantum-inspired latent modeling can serve as a novel pathway for computational creativity in music information retrieval. Beyond generation, the work discussed the implications of quantum latent structures for music representation learning, proposing a foundation for future hybrid quantum-classical MIR frameworks capable of handling musical complexity through the principles of superposition and entanglement.