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IEEE International Conference on Quantum Computing and Engineering (QCE)

Modeling Neuronal Activity with Quantum Generative Adversarial Networks

Vinicius Hernandes, Eliška Greplová

Published Sep 17, 2023• 1 citation
Publisher

Abstract

Understanding the information processing in neuronal networks relies on the development of computational models that accurately reproduce their activity data. Machine learning techniques have shown promising results in generating synthetic neuronal data, but interpretability remains an issue due to a large number of parameters requiring fitting. Quantum machine learning models, particularly quantum generative learning, are emerging as more compact alternatives that offer similar outcomes. This study presents an efficient framework for generating synthetic neuronal data using a Quantum Generative Adversarial Network (QGAN), with a quantum generator and a classical discriminator. We tested the proposed framework for the minimal case of two neurons, considering the case of single time-steps. Preliminary results demonstrate the QGAN's capability to achieve reliable outcomes with a reduced number of trainable parameters, scaling efficiently for increasing neuronal network sizes. The model effectively captures spiking frequencies of real data, although further refinement is required to incorporate temporal correlations for more extended time-steps. Despite certain limitations, this study lays the foundation for future advancements in using quantum adversarial generative networks to model neuronal activity. The promising potential of QGANs in this domain highlights the possibility of gaining valuable insights into the functioning of complex biological systems through quantum-inspired computational methods.

DOI
10.1109/qce57702.2023.10267

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This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for the generation of synthetic data. Traditional Generative Adversarial Networks (GANs) often face challenges in producing high-quality synthetic data, especially when dealing with complex data distributions. We propose a novel framework utilizing quantum circuits to construct both the generator and discriminator within a QGAN architecture. The core innovation lies in harnessing quantum superposition and interference during the adversarial training process to potentially improve the realism and diversity of the generated synthetic data. Our approach introduces a theoretical foundation and a conceptual design for QGANs, paving the way for future research and development in this emerging field. The primary goal is to demonstrate the potential benefits of incorporating quantum mechanical principles into the GAN framework, leading to enhanced synthetic data generation capabilities. The performance of a QGAN is expected to be superior to a classical GAN due to the unique properties of quantum systems.
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Generative Adversarial Networks (GANs) have demonstrated remarkable success in generating realistic data across various domains. However, traditional GANs often struggle with mode collapse, difficulty in capturing complex data distributions, and challenges in generating high-dimensional data effectively. This paper proposes Quantum-Inspired Generative Adversarial Networks (QGANs), a novel approach that incorporates principles of quantum mechanics – specifically superposition and interference – into the GAN architecture. QGANs utilize quantum circuits to generate latent vectors in a superposition state, enabling richer sampling and enhanced diversity in the generated data. Interference patterns are then strategically applied to refine the generated output, leading to improved quality and complexity. We argue that this direct application of quantum concepts offers a promising avenue for overcoming the limitations of classical GANs and achieving superior generative performance. This work presents a foundational framework for QGANs, outlining the core mechanisms and potential benefits, and lays the groundwork for future research in this area.