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Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing

arXiv:2603.23984v1 Announce Type: new Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions, which imposes a bottleneck on the representational capacity of deep-learning models, making it difficult to capture the complex and non-stationary dynamics of seismic wavefields. Different from the classical perceptron-stacked NNs which are fundamentally confined to real-valued Euclidean spaces, the quantum NNs leverage the exponential state space of quantum mechanics to map the features into high-dimensional Hilbert spaces, transcending the representational boundary of classical NNs. Based on this insight, we propose a quantum-classical synergistic generative adversarial network (QC-GAN) for seismic data processing, serving as the first application of quantum NNs in seismic

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Zhengyi Yuan, Xintong Dong, Xinyang Wang, Zheng Cong, Shiqi Dong · · 1 min read · 2 views
#cs.LG #physics.geo-ph

arXiv:2603.23984v1 Announce Type: new Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions, which imposes a bottleneck on the representational capacity of deep-learning models, making it difficult to capture the complex and non-stationary dynamics of seismic wavefields. Different from the classical perceptron-stacked NNs which are fundamentally confined to real-valued Euclidean spaces, the quantum NNs leverage the exponential state space of quantum mechanics to map the features into high-dimensional Hilbert spaces, transcending the representational boundary of classical NNs. Based on this insight, we propose a quantum-classical synergistic generative adversarial network (QC-GAN) for seismic data processing, serving as the first application of quantum NNs in seismic exploration. In QC-GAN, a quantum pathway is used to exploit the high-order feature correlations, while the convolutional pathway specializes in extracting the waveform structures of seismic wavefields. Furthermore, we design a QC feature complementarity loss to enforce the feature orthogonality in the proposed QC-GAN. This novel loss function can ensure that the two pathways encode non-overlapping information to enrich the capacity of feature representation. On the whole, by synergistically integrating the quantum and convolutional pathways, the proposed QC-GAN breaks the representational bottleneck inherent in classical GAN. Experimental results on denoising and interpolation tasks demonstrate that QC-GAN preserves wavefield continuity and amplitude-phase information under complex noise conditions.

Executive Summary

This article proposes a novel quantum-classical synergistic generative adversarial network (QC-GAN) for seismic data processing, leveraging the exponential state space of quantum mechanics to transcend the representational capacity of classical neural networks. The QC-GAN integrates a quantum pathway to exploit high-order feature correlations with a convolutional pathway to extract waveform structures of seismic wavefields. A novel loss function, QC feature complementarity loss, is designed to enforce feature orthogonality, ensuring non-overlapping information representation. Experimental results demonstrate the QC-GAN's effectiveness in preserving wavefield continuity and amplitude-phase information under complex noise conditions, breaking the representational bottleneck of classical GANs. This breakthrough has significant implications for seismic exploration and data processing, and its potential applications extend to other fields where complex data representation is critical.

Key Points

  • ▸ QC-GAN proposes a novel approach to seismic data processing by integrating quantum and convolutional pathways.
  • ▸ The QC-GAN leverages the exponential state space of quantum mechanics to transcend the representational capacity of classical neural networks.
  • ▸ A novel loss function, QC feature complementarity loss, is designed to enforce feature orthogonality and ensure non-overlapping information representation.

Merits

Strength in Representational Capacity

The QC-GAN's integration of quantum and convolutional pathways enables it to represent complex seismic wavefields with greater fidelity and accuracy than classical GANs.

Demerits

Technical Complexity

The QC-GAN's reliance on quantum mechanics and novel loss functions may introduce significant technical complexity, potentially limiting its adoption and deployment in real-world applications.

Expert Commentary

This article represents a significant breakthrough in the field of seismic data processing, leveraging the power of quantum mechanics to transcend the representational capacity of classical neural networks. The QC-GAN's integration of quantum and convolutional pathways offers a novel approach to complex data representation, with significant implications for seismic exploration and data processing. However, the technical complexity of the QC-GAN may limit its adoption and deployment in real-world applications. As such, further research and development are necessary to fully realize the potential of the QC-GAN.

Recommendations

  • ✓ Further research is needed to refine the QC-GAN's architecture and optimize its performance in real-world applications.
  • ✓ The development of more advanced loss functions and optimization techniques is necessary to fully leverage the representational capacity of the QC-GAN.

Sources

Original: arXiv - cs.LG

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