# Generative Music with Partitioned Quantum Cellular Automata
## 1 Introduction  
In this paper, we report on our research into harnessing quantum computing for creating original music.  
The great majority of research in artificial intelligence (AI) for musical composition has been focused on developing systems for imitating the style of existing music [1]. For instance, [PERSON]. [2] introduced a system that analysed given pieces of music and recombined the identified musical elements into new pieces, which sounded like the originals. This system used a recombination algorithm informed by augmented transition networks (ATN). These were originally developed for linguistics to analyse the structure of sentences [3]. More recently, ref. [4] introduced a system based on deep learning neural networks [5] to compose tunes imitating the ones used for training the network. An overview of deep learning models for music composition is available in [6].  
Indeed, state-of-the-art AI is capable of generating convincing musical compositions [1, 7]. However, the work reported here is not aimed at systems to imitate existing repertoires. We are interested in developing AI technology to support the creation of innovative music instead. Examples of previous work in this vein are sparse. A notable system, _Fractal Music_[8], generates music from fractals [9]. Moreover, [10] introduced _CAMUS_, a system that generated music using cellular automata (CA) [11]. This system inspired the development of the systems reported in the present paper.  
Quantum computing is an emergent technology, which is advancing rapidly. It promises a number of advantages for certain computational tasks, which current digital processors struggle to realize. For instance, the simulation of molecules for drug discoveryand development is computationally very demanding, even for current supercomputers. Quantum computing brings new approaches to optimise this, which will enable drugs to be developed faster and more effectively [12].  
For music, quantum computers will bring considerable processing speed and capacity to handle increased quantities of data simultaneously. This is bound to impact future music recommendation systems [13]. Furthermore, and perhaps more significantly, this new technology is bound to foster new approaches to making music, which would be unlikely otherwise. For instance, whereas ref. [14] developed a quantum computing music sampler called _QuiKo_, ref. [15] developed the concept of a _Qeyboard_, a quantum musical instrument. Moreover, [16] developed _Quantum Music Playground_, an educational tool for learning quantum computing concepts through music. This paper introduces a system for composing music using partitioned quantum cellular automata (PQCA) [17]. It follows from preliminary work introduced in [18].  
Music is the art of organising sounds in space and time [19; 20]. CA are abstract computational models of systems that change some features with space and time [11]. Pragmatically, CA models are useful because they generate patterns of data that can be translated into music [21; 22; 23]. Quantum versions of CA have been developed to simulate quantum mechanical phenomena; e.g., quantum lattice gases [24], hence our rationale for exploring PQCA to generate music.  
We begin with a brief explanation of quantum computing, CA and quantum CA. Then, we present the new PQCA models that we developed for musical composition and show practical examples produced for a musical composition entitled _Qubism_ We end the paper with concluding remarks and directions for further development.