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 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.
Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-based optimization of a larger number of quantum circuit parameters. The major drawback is that such quantum gradient calculation requires a large amount of circuit evaluation, posing challenges in current near-term quantum hardware and simulation software. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea to this RC approach is that the QRNN with randomly initialized weights is treated as a dynamical system and only the final classical linear layer is trained. Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series prediction tasks. Since the QRNN training complexity is significantly reduced, the proposed model trains notably faster. In this work we also compare to corresponding classical RNN-based RC implementations and show that the quantum version learns faster by requiring fewer training epochs in most cases. Our results demonstrate a new possibility to utilize quantum neural network for sequential modeling with greater quantum hardware efficiency, an important design consideration for noisy intermediate-scale quantum (NISQ) computers.
Quantum computing and artificial intelligence, two prominent topics in science and technology, are rapidly advancing and extending their influence into numerous fields, including music. Quantum computer music, which merges the strengths of quantum computing and deep learning, heralds a new era in the integration of music creation with cutting-edge technology. The interactive quantum music composition “Spinnings—Q1 Synth Trio”, created by Brazilian composer Miranda during the QuTune project at the Interdisciplinary Centre for Computer Music Research (ICCMR), in collaboration with the University of Oxford, stands as a notable example of quantum computer music. This study adopts a case study approach to thoroughly investigate the technical creative process behind this work, covering elements such as quantum computing, quantum properties, qubits, quantum gates, and quantum circuits, gradually unveiling the mathematical logic behind quantum algorithmic composition. The results of this study indicate that quantum algorithmic composition, as an emerging approach to music creation, not only generates unique music through the characteristics of quantum computing but also offers new possibilities for the integration of music, art, and technology. By applying quantum bits, quantum gates, and quantum circuits, this research demonstrates how quantum computing can provide new theoretical foundations and practical methods for music composition. Furthermore, the study discusses how to optimize the interactive creative experience in quantum music works and how to enhance the understanding and appreciation of quantum music among a broader audience of musicians and listeners. With the continuous advancement of quantum computing technology, quantum music is poised to contribute a distinctive dimension to the global flourishing of musical culture. This research offers fresh perspectives and ideas for the development of this field.
Quantum reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities. Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs to address computationally challenging tasks with enhanced efficiency and accuracy. To this end, we employ an open quantum system model comprising two-level atomic ensembles coupled to Lorentzian photonic cavities to construct a quantum physical reservoir computer layer for a recurrent neural network. We evaluate the effectiveness of this approach by applying it to a standard machine learning image-recognition problem and benchmarking its performance against a conventional neural network of similar architecture, but lacking the quantum physical reservoir computer layer. Remarkably, as the dataset size increases, the quantum physical reservoir computer outperforms the conventional neural network, requiring fewer training epochs and a smaller dataset to achieve comparable accuracy. Furthermore, we employ the quantum physical reservoir computing approach to model the dynamics of open quantum systems, focusing on atomic system ensembles interacting with a structured photonic reservoir associated with a photonic band-gap material. Our results reveal that the quantum reservoir computer provides equally powerful representations for quantum dynamical problems, maintaining effectiveness even under constraints of limited training data.
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 'blackbox' 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.
Quantum Reservoir Computing (QRC) has emerged as a powerhouse for Noisy Intermediate-Scale Quantum (NISQ) machine learning, offering the ability to process complex temporal data with minimal training overhead by leveraging the high-dimensional dynamics of quantum states. This paper introduces QRC-Lab, an open-source, modular Python framework designed specifically to facilitate the transition from theoretical quantum dynamics to applied machine learning research. We provide a rigorous definition of QRC, contrasting physical and gate-based approaches, and detail the mathematical foundations of the reservoir mapping. QRC-Lab’s gate-based architecture is presented as a flexible laboratory for exploring data encoding, reservoir connectivity, and measurement strategies. We validate the framework through diverse case studies, including Short-Term Memory capacity, non-linear Parity check, and NARMA-type forecasting. Furthermore, we discuss the framework’s future roadmap towards multi-platform hardware support and its role in visualizing theoretical risk bounds, positioning QRC-Lab as a vital tool for the next generation of quantum computing education.
Reservoir computing (RC) is an effective method for predicting chaotic systems by using a high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning phase linear, which simplifies training and reduces computational complexity compared to fully trained recurrent neural networks (RNNs). Quantum reservoir computing (QRC) uses the exponential growth of Hilbert spaces in quantum systems, allowing for greater information processing, memory capacity, and computational power. However, the original QRC proposal requires coherent injection of inputs multiple times, complicating practical implementation. We present a hybrid quantum-classical approach that implements memory through classical post-processing of quantum measurements. This approach avoids the need for multiple coherent input injections and is evaluated on benchmark tasks, including the chaotic Mackey-Glass time series prediction. We tested our model on two physical platforms: a fully connected Ising model and a Rydberg atom array. The optimized model demonstrates promising predictive capabilities, achieving a higher number of steps compared to previously reported approaches.