A Quantum Natural Language Processing Approach to Musical Intelligence

Miranda, Eduardo Reck; Yeung, Richie; Pearson, Anna; Meichanetzidis, Konstantinos; Coecke, Bob

doi:10.1007/978-3-031-13909-3_13

Cited by 8 publications

(3 citation statements)

References 43 publications

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“…Then, we built a proof-of-concept generative music system, named Quanthoven, which called upon the classifier to generate music. Quanthoven recombined snippets extracted from the music in the original training corpus to produce new rhythmic and melodic pieces [80]. A few examples were released on SoundClick [71].…”

Section: Other Initiatives and Concluding Remarksmentioning

confidence: 99%

The advent of quantum computer music: mapping the field

Miranda

2024

Rep. Prog. Phys.

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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.

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Section: Other Initiatives and Concluding Remarksmentioning

confidence: 99%

The advent of quantum computer music: mapping the field

Miranda

2024

Rep. Prog. Phys.

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“…In the CQ context, there are attempts to use quantum computers, quantum channels [32] and states [33][34][35] for tasks where classical RNNs are often used, such as natural language processing [36][37][38][39]. In this context, there are already several quantisations of RNNs [40][41][42][43][44], usually exploiting a substitution of the feed-forward NNs in RNNs, LSTMs or GRUs with some form of QNN.…”

Section: Introductionmentioning

confidence: 99%

Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks

Bondarenko¹,

Salzmann²,

Schmiesing³

2023

Preprint

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Recurrent neural networks play an important role in both research and industry. With the advent of quantum machine learning, the quantisation of recurrent neural networks has become recently relevant. We propose fully quantum recurrent neural networks, based on dissipative quantum neural networks, capable of learning general causal quantum automata. A quantum training algorithm is proposed and classical simulations for the case of product outputs with the fidelity as cost function are carried out. We thereby demonstrate the potential of these algorithms to learn complex quantum processes with memory in terms of the exemplary delay channel, the time evolution of quantum states governed by a time-dependent Hamiltonian, and highand low-frequency noise mitigation. Numerical simulations indicate that our quantum recurrent neural networks exhibit a striking ability to generalise from small training sets.

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“…String diagrams provide an intuitive language for quantum processes [16,17,18,19,20] and are implicitly employed in quantum software packages such as tket [21], PyZX [22], lambeq [23], DisCoPy [24], Quanhoven [25]. On the one hand, Coecke and Duncan [26] introduced the ZX calculus, a graphical language for reasoning about qubit quantum computing, with applications in circuit-based [27], measurement-based [28], and fault tolerant [29] quantum computing.…”

Section: Introductionmentioning

confidence: 99%