Code and resources for Quanthoven

This repository holds the code and the datasets for the paper:

• Miranda, Yeung, Pearson, Meichanetzidis, Coecke (2021). A Quantum Natural Language Processing Approach to Musical Intelligence

This paper pioneers a Quantum Natural Language Processing approach to classifying music. Using this quantum classifier we use a generate and test approach to generate quantum music. This is a proof of concept, but as quantum devices improve in size and fidelity we will be able to learn a quantum classifier that would be hard to simulate on a classical device.

Contents

• audio contains audio renderings of the training, development and testing data. Rendered from MIDI files using Pianoteq.
• compositions contains the scores (PDF) and recordings of the 4 pieces discussed in the chapter, by pianist Lauryna Sableviciute.
• datasets contains the train / development / test set used for our experiment. The generation methodology is described in the paper.
• experiment.ipynb contains the pipeline described in Fig 9. of the paper, which is used used to learn the dataset.

Code requirements

For running the code, you will need Python 3.7 or later. Further, the following packages must also be installed:

• discopy (v0.3.7.1)
• lambeq (v0.1.2)

Links

For further help see:

• for discopy: https://discopy.readthedocs.io/en/main/index.html
• for lambeq: https://cqcl.github.io/lambeq/