QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
Circuit-centric quantum classifiers
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A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.
citing papers explorer
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QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
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Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.