Affiliation:
1. Interdisciplinary Centre for Computer Music Research (ICCMR), University of Plymouth, Plymouth PL4 8AA, UK
2. Quantinuum, Partnership House, Carlisle Place, London SW1P 1BX, UK
Abstract
Cellular automata (CA) are abstract computational models of dynamic systems that change some features with space and time. Music is the art of organising sounds in space and time, and it can be modelled as a dynamic system. Hence, CA are of interest to composers working with generative music. The art of generating music with CA hinges on the design of algorithms to evolve patterns of data and methods to render those patterns into musical forms. This paper introduces methods for creating original music using partitioned quantum cellular automata (PQCA). PQCA consist of an approach to implementing CA on quantum computers. Quantum computers leverage properties of quantum mechanics to perform computations differently from classical computers, with alleged advantages. The paper begins with some explanations of background concepts, including CA, quantum computing, and PQCA. Then, it details the PQCA systems that we have been developing to generate music and discusses practical examples. PQCA-generated materials for Qubism, a professional piece of music composed for London Sinfonietta, are included. The PQCA systems presented here were run on real quantum computers rather than simulations thereof. The rationale for doing so is also discussed.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference50 articles.
1. Miranda, E.R. (2021). Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity, Springer International Publishing.
2. Cope, D. (1996). Experiments in Musical Intelligence, A-R Editions.
3. Transition network grammars for natural language analysis;Woods;Commun. ACM,1970
4. Popular Song Composition Based on Deep Learning and Neural Networks;Kuang;J. Math.,2021
5. Graupe, D. (2016). Deep Learning Neural Networks, World Scientific.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献