# A Quantum Natural Language Processing Approach to Musical Intelligence

arXiv: https://arxiv.org/abs/2111.06741 · HTML: https://mlsanity.com/paper/2111.06741/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [lambeq: An Efficient High-Level Python Library for Quantum NLP](https://mlsanity.com/paper/2110.04236/) (2021)
- [Quantum Natural Language Processing on Near-Term Quantum Computers](https://mlsanity.com/paper/2005.04147/) (2020)
- [A* CCG Parsing with a Supertag and Dependency Factored Model](https://mlsanity.com/paper/1704.06936/) (2017)
- [A CCG-Based Version of the DisCoCat Framework](https://mlsanity.com/paper/2105.07720/) (2021)
- [Lambek vs. Lambek: Functorial Vector Space Semantics and String Diagrams for Lambek Calculus](https://mlsanity.com/paper/1302.0393/) (2013)
- [AI Methods in Algorithmic Composition: A Comprehensive Survey](https://mlsanity.com/paper/1402.0585/) (2014)
- [Compositionality as we see it, everywhere around us](https://mlsanity.com/paper/2110.05327/) (2021)
- [Diagrammatic Differentiation for Quantum Machine Learning](https://mlsanity.com/paper/2103.07960/) (2021)
- [Assisted music creation with Flow Machines: towards new categories of new](https://mlsanity.com/paper/2006.09232/) (2020)

## Built on by (most cited first, 11 of 11)

- [Quantum Natural Language Generation on Near-Term Devices](https://mlsanity.com/paper/2211.00727/) (2022)
- [Large scale analysis of gender bias and sexism in song lyrics](https://mlsanity.com/paper/2208.02052/) (2022)
- [Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit](https://mlsanity.com/paper/2305.19383/) (2023)
- [Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2301.08167/) (2023)
- [Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach](https://mlsanity.com/paper/2312.00688/) (2023)
- [Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing](https://mlsanity.com/paper/2405.11783/) (2024)
- [Investigating the usefulness of Quantum Blur](https://mlsanity.com/paper/2112.01646/) (2021)
- [SQL2Circuits: Estimating Cardinalities, Execution Times, and Costs for SQL Queries with Quantum Natural Language Processing](https://mlsanity.com/paper/2306.08529/) (2023)
- [Quantum Methods for Managing Ambiguity in Natural Language Processing](https://mlsanity.com/paper/2504.00040/) (2025)
- [Quantum Concept Music Score from Quantum Picturalism: Musical Incarnation of a Bell-Pair under Measurements](https://mlsanity.com/paper/2510.05391/) (2025)
- [Quantum NLP models on Natural Language Inference](https://mlsanity.com/paper/2510.15972/) (2025)

## Abstract (arXiv)

There has been tremendous progress in Artificial Intelligence (AI) for music, in particular for musical composition and access to large databases for commercialisation through the Internet. We are interested in further advancing this field, focusing on composition. In contrast to current black-box AI methods, we are championing an interpretable compositional outlook on generative music systems. In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars. Quantum computing is a nascent technology, which is very likely to impact the music industry in time to come. Thus, we are pioneering a Quantum Natural Language Processing (QNLP) approach to develop a new generation of intelligent musical systems. This work follows from previous experimental implementations of DisCoCat linguistic models on quantum hardware. In this chapter, we present Quanthoven, the first proof-of-concept ever built, which (a) demonstrates that it is possible to program a quantum computer to learn to classify music that conveys different meanings and (b) illustrates how such a capability might be leveraged to develop a system to compose meaningful pieces of music. After a discussion about our current understanding of music as a communication medium and its relationship to natural language, the chapter focuses on the techniques developed to (a) encode musical compositions as quantum circuits, and (b) design a quantum classifier. The chapter ends with demonstrations of compositions created with the system.