Quantum computing can be employed in computer-aided music composition to control various attributes of the music at different structural levels. This article describes the application of quantum simulation to model compositional decision making, the simulation of quantum particle tracking to produce noise-based timbres, the use of basis state vector rotation to cause changing probabilistic behaviors in granular harmonic textures, and the exploitation of quantum measurement error to cause noisy perturbations of spatial soundpaths. We describe the concepts fundamental to these techniques, we provide algorithms and software enacting them, and we provide examples demonstrating their implementation in computer-generated music
Quantum computing can be employed in computer-aided music composition to control various attributes of the music at different structural levels. This article describes the application of quantum simulation to model compositional decision making, the simulation of quantum particle tracking to produce noise-based timbres, the use of basis state vector rotation to cause changing probabilistic behaviors in granular harmonic textures, and the exploitation of quantum measurement error to cause noisy perturbations of spatial soundpaths. We describe the concepts fundamental to these techniques, we provide algorithms and software enacting them, and we provide examples demonstrating their implementation in computer-generated music
With the emergence of quantum computers, a new field of algorithmic music composition has been initiated. The vast majority of previous work focuses on music generation using gate-based quantum computers. An alternative model of computation is adiabatic quantum computing (AQC), and a heuristic algorithm known as quantum annealing running in the framework of AQC is a promising method for solving optimization problems. In this chapter, we lay the groundwork of music composition using quantum annealing. We approach the process of music composition as an optimization problem. We describe the fundamental methodologies needed for generating different aspects of music including melody, rhythm, and harmony. The discussed techniques are illustrated through examples to ease the understanding. The music pieces generated using D-Wave quantum annealers are among the first examples of their kind and presented within the scope of the chapter. The text is an unedited pre-publication version of a chapter which will appear in the book "Quantum Computer Music", Miranda, E. R. (Editor).
The composer constantly makes choices: it is the composer, in fact, who chooses a melodic, rhythmic, harmonic path among the various, infinite possible paths between point A, the beginning, and point B, the end of a composition. The choice of one path over another, a local modification of a given path, can also be influenced by external factors. As it happens for the path of a charged particle in a magnetic field, the choice of the musical path can be influenced by personal style, culture, technical characteristics of the instruments, and combinations of instruments used. Even in quantum mechanics, there are “choices”: the state of a system appears as a superposition of possible eigenstates with different probability amplitudes. The act of measurement “forces” the system to choose a particular state: the observer influences the physics of the system. Like the famous example of Schrödinger’s cat, alive and dead at the same time until the box in which it is located is opened. From the choices of notes and musical paths to decision-making systems, we present some examples of the application of quantum mechanics to music. Among the applications of quantum computing, we mention a study on robots with associated musical descriptions. In particular, we consider the distributed intelligence of a swarm of robots and its possible musical description, from some existing works to our original application.
Music is a multifaceted art form that requires a nuanced and comprehensive framework for analysis. This framework should encompass correlations among diverse musical attributes, including melody, harmony, rhythm, timbre, and dynamics. However, modeling the interaction between these attributes is anticipated to require substantial computational resources. In this study, we propose a Bayesian model to represent various musical attributes and explore its implementation and inference on a quantum computer. We show how to translate the Bayesian model to a quantum circuit and perform inference using the quantum rejection sampling algorithm on a quantum simulator. Through empirical evaluation utilizing a real-world dataset, we compare the performance of the quantum algorithm against classical rejection sampling. Our results reveal that the quantum method improves the acceptance rate of generated samples, thereby enhancing posterior probability estimates.
This paper presents an approach to developing computer-aided music composition (CAMC) systems with quantum computing. CAMC systems are aimed at supporting musicians in creating music rather than generating complete pieces of music autonomously. For instance, they may generate ideas or prompts to be further developed during the creative process. We developed a CAMC system using a machine learning method called Quantum Reservoir Computing (QRC) that learns to create tunes in the style of given examples. The system proved to run satisfactorily on the Noisy Intermediate-Scale Quantum (NISQ) machines available today. It does not require large amounts of qubits to outperform equivalent classical Deep Learning methods. Furthermore, it can serve the purposes of a CAMC system without the need for big data for training. This paper provides a general introduction to reservoir computing fundamentals and explains how quantum mechanics can harness its performance. Then, it walks the reader through the development of our system and presents demonstrations. A comparative experiment against a classic Recurrent Neural Network (RNN) model is also discussed.
This chapter examines the Variational Quantum Harmonizer, a software tool and musical interface that focuses on the problem of sonification of the minimization steps of Variational Quantum Algorithms (VQA), used for simulating properties of quantum systems and optimization problems assisted by quantum hardware. Particularly, it details the sonification of Quadratic Unconstrained Binary Optimization (QUBO) problems using VQA. A flexible design enables its future applications both as a sonification tool for auditory displays in scientific investigation, and as a hybrid quantum-digital musical instrument for artistic endeavours. In turn, sonification can help researchers understand complex systems better and can serve for the training of quantum physics and quantum computing. The VQH structure, including its software implementation, control mechanisms, and sonification mappings are detailed. Moreover, it guides the design of QUBO cost functions in VQH as a music compositional object. The discussion is extended to the implications of applying quantum-assisted simulation in quantum-computer aided composition and live-coding performances. An artistic output is showcased by the piece \textit{Hexagonal Chambers} (Thomas and Itaboraí, 2023).
Quantum computing technology is developing at a fast pace. The impact of quantum computing on the music industry is inevitable. This paper maps the emerging field of quantum computer music. Quantum computer music investigates, and develops applications and methods to process music using quantum computing technology. The paper begins by contextualising the field. Then, it discusses significant examples of various approaches developed to date to leverage quantum computing to learn, process and generate music. The methods discussed range from rendering music using data from physical quantum mechanical systems and quantum mechanical simulations to computational quantum algorithms to generate music, including quantum AI. The ambition to develop techniques to encode audio quantumly for making sound synthesisers and audio signal processing systems is also discussed.