This project explores how Quantum Fourier Transform (QFT) can be applied to music signal compression, leveraging quantum principles to efficiently represent and compress audio waveforms.
By simulating QFT using quantum computing libraries, the project compares quantum-based frequency domain transformations with classical Fourier approaches (FFT).
- Implement Quantum Fourier Transform (QFT) using Python (Qiskit or similar library).
- Compress and reconstruct music/audio signals using QFT circuits.
- Compare quantum vs. classical signal compression in terms of fidelity and efficiency.
- Demonstrate potential benefits of quantum-based data compression.
- Python 3.10+
- Qiskit – for quantum circuit simulation
- NumPy / SciPy – for signal processing
- Matplotlib / Librosa – for visualization and waveform analysis
- Jupyter Notebook – for testing and results presentation
| Metric | Classical FFT | Quantum QFT |
|---|---|---|
| Computation Complexity | O(N log N) | O((log N)²) theoretically |
| Output Fidelity | High | Comparable |
| Compression Ratio | Moderate | Potentially higher (for quantum states) |
📈 Results The Quantum Fourier Transform successfully compresses music signals into smaller quantum states.
The reconstructed signals maintain high similarity to the original waveform.
Demonstrates quantum advantage potential for data representation and audio compression.
📚 References Nielsen, M. & Chuang, I. Quantum Computation and Quantum Information
Qiskit Documentation: https://qiskit.org/documentation/
Librosa Audio Analysis Toolkit
👩💻 Author Ram Lasya
GitHub: @ramlasyaa
Project: Quantum Fourier Transform for Music Signal Compression
💡 License This project is licensed under the MIT License.