Project Complete

QuantumMelody

Can quantum-inspired models be used to analyze and compare vocal performance by encoding musical features related to pitch, dynamics, and timbre?

Open-source codeBridges quantum computing and musicComplete, end-to-end study

First Award

Synopsys Science Fair · 2025

Qualifier

CSEF · 2025

Mayor's Recognition

City of Cupertino · 2026

QuantumMelody explores quantum and quantum-inspired computational models for analyzing vocal music performance.

Encoding: Musical features like pitch stability, timing, dynamics, and timbral characteristics are mapped into structured quantum circuits
Comparison: Side-by-side analysis between performers, or between a student and a reference performance
Why quantum: Grouping features and using entanglement within feature sets allows the study to capture relationships that are hard to represent with classical models alone
Conclusion: Subject-matter entanglement can better analyze quality of vocal singing and contrast “master” and “student” tracks

Research at a glance

Primary methods

Data sources

Extracted audio features from vocal recordings
pitch, timing, dynamic, and timbral descriptors derived from signal processing pipelines

Outputs