The paper defines QNN expressivity as the effective rank of the Fisher information matrix and shows numerically that this rank can reach its maximum 4^n-1 when data, measurement, and circuit are jointly optimized, then uses the rank as a reward for automated circuit design.
Quantum Recurrent Neural Networks for Sequential Learning,
4 Pith papers cite this work, alongside 55 external citations. Polarity classification is still indexing.
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The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.
A rescaled feature map (2*x-1.5 on normalized inputs) combined with a standard variational circuit is claimed to reach 100% accuracy on a two-class mobile data usage task while using fewer gates than the IBM tutorial baseline.
citing papers explorer
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Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
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A Comprehensive Analysis of Accuracy and Robustness in Quantum Neural Networks
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.