ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks

• URL: http://arxiv.org/abs/2503.21815v1
• Date: Wed, 26 Mar 2025 01:14:26 GMT
• Title: ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks
• Authors: Mohamed Afane, Gabrielle Ebbrecht, Ying Wang, Juntao Chen, Junaid Farooq,
• Abstract summary: We introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimize data complexity for efficient computations in Quantum Neural Networks (QNNs)<n>ATP dynamically prunes non-essential features in the data based on adaptive thresholds, effectively reducing quantum circuit requirements while preserving high performance.<n>Our results highlight ATPs ability to balance computational efficiency and model resilience, achieving significant performance improvements with fewer resources.
• Score: 6.80372007036868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Neural Networks (QNNs) offer promising capabilities for complex data tasks, but are often constrained by limited qubit resources and high entanglement, which can hinder scalability and efficiency. In this paper, we introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimizes data complexity for efficient computations in QNNs. ATP dynamically prunes non-essential features in the data based on adaptive thresholds, effectively reducing quantum circuit requirements while preserving high performance. Extensive experiments across multiple datasets demonstrate that ATP reduces entanglement entropy and improves adversarial robustness when combined with adversarial training methods like FGSM. Our results highlight ATPs ability to balance computational efficiency and model resilience, achieving significant performance improvements with fewer resources, which will help make QNNs more feasible in practical, resource-constrained settings.

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