README.md

Maqenta

Magenta, but in Quantum.

Roadmap

1. Learn about GANs

   • Watch Ian Goodfellow: Generative Adversarial Networks.
   • Watch Seth Lloyd: Quantum Generative Adversarial Networks.
   • Go through PennyLane's Tutorial of QuGANs.
   • Chris Olah - Understanding LSTM Networks
   • Go through GANSynth tutorial on Magenta.
   • Port GANSynth to Quantum.

2. Learn about RNNs & LSTMs

   • Implement Quantum Long Short-Term memory in PennyLane.
   • Implement a music generation algorithm using the above.
   • Go through RNN music generation tutorial on Magenta.
   • Port the above to Quantum.

3. Learn more about other QML Methods that could be possible for generating music.

   • Quanvolutional Neural Networks.
   • Quantum transfer learning.
   • Quantum models as Fourier series.
   • Learning to learn with quantum neural networks.
   • Quantum support vector machine for big data classification.

4. Learn Music Theory to be able to find even more ideas for generating Quantum Music.

   • Learn music theory in half an hour.
   • Music And Measure Theory - 3Blue1Brown.

5. Read these papers

   • Recurrent Quantum Neural Networks.
   • Generative Adversarial Networks.
   • Progressive Growing of GANs for Improved Quality, Stability, and Variation.
   • Adversarial Audio Synthesis.
   • GANSynth: Adversarial Neural Audio Synthesis.

6. Future Development.

   • Develop the code for EQGAN.
   • Change the measurements from global to local in the QuGAN module, because it gets trapped inside a barren plateu very quickly.
   • The Issue of monotony (same offset between notes).
   • Using a tunable vector w to be inner producted with the output of the functions, basically adding a classical layer that needs to be optimized.
   • Cross-check to see if there's any difference in tuning a matrix to generate multiple next notes or just a vector to finding one next note.