A quantum reservoir computing approach to computer-aided music composition

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.