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Vocal singing is a profoundly emotional art form possibly predating spoken language, yet evaluating a vocal track remains a subjective and specialized task. Meanwhile, quantum computing shows promise to bring about significant advances in science and art. This study introduces QuantumMelody, a quantum-enhanced algorithm to evaluate vocal performances through objective metrics. QuantumMelody begins by collecting a comprehensive array of classical acoustic and musical features including pitch contours, formant frequencies, Mel-spectrograms, and dynamic ranges. These features are divided into three musically categorized groups, converted into scaled angles based on statistical metrics, and then encoded into specific quantum rotation gates. Each qubit group is entangled internally, followed by intergroup entanglement, thus exploring subtle, non-linear relationships within and across feature sets. The resulting quantum probability distributions and classical features are used to train a neural network, combined with a spectrogram transformer to holistically grade each recording on a 2--5 scale. Key difference metrics like the Jensen-Shannon distance and Euclidean measures of scaled angles are used to enable nuanced comparisons of different recordings. Furthermore, the algorithm uses classical music-based heuristics to provide targeted suggestions to the user for various aspects of vocal technique. On a dataset of 168 labeled 20 second vocal excerpts, QuantumMelody achieves 74.29% agreement with expert graders. The circuits are simulated; we do not claim hardware speedups, and results reflect a modest, single-domain dataset. We position this as an applied audio-signal-processing contribution and a feasibility step toward objective, interpretable feedback in singing assessment.