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Found 5 papers, 1 papers with code
QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems
Subsequently, QuanTest formulates the problem of generating test inputs that maximize the quantum entanglement sufficiency and capture incorrect behaviors of the QNN system as a joint optimization problem and solves it in a gradient-based manner to generate quantum adversarial examples.
GQHAN: A Grover-inspired Quantum Hard Attention Network
In response to the dilemma of HAM and QML, a Grover-inspired Quantum Hard Attention Mechanism (GQHAM) consisting of a Flexible Oracle (FO) and an Adaptive Diffusion Operator (ADO) is proposed.
QKSAN: A Quantum Kernel Self-Attention Network
Self-Attention Mechanism (SAM) excels at distilling important information from the interior of data to improve the computational efficiency of models.
QSAN: A Near-term Achievable Quantum Self-Attention Network
Self-Attention Mechanism (SAM) is good at capturing the internal connections of features and greatly improves the performance of machine learning models, espeacially requiring efficient characterization and feature extraction of high-dimensional data.
A New Multi-input Model with the Attention Mechanism for Text Classification
), we propose a new text classification model, which uses words, characters, and labels as input.
