  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 novel Quantum Self-Attention Network (QSAN) is
proposed for image classification tasks on near-term quantum devices. First, a
Quantum Self-Attention Mechanism (QSAM) including Quantum Logic Similarity
(QLS) and Quantum Bit Self-Attention Score Matrix (QBSASM) is explored as the
theoretical basis of QSAN to enhance the data representation of SAM. QLS is
employed to prevent measurements from obtaining inner products to allow QSAN to
be fully implemented on quantum computers, and QBSASM as a result of the
evolution of QSAN to produce a density matrix that effectively reflects the
attention distribution of the output. Then, the framework for one-step
realization and quantum circuits of QSAN are designed for fully considering the
compression of the measurement times to acquire QBSASM in the intermediate
process, in which a quantum coordinate prototype is introduced as well in the
quantum circuit for describing the mathematical relation between the output and
control bits to facilitate programming. Ultimately, the method comparision and
binary classification experiments on MNIST with the pennylane platform
demonstrate that QSAN converges about 1.7x and 2.3x faster than
hardware-efficient ansatz and QAOA ansatz respevtively with similar parameter
configurations and 100% prediction accuracy, which indicates it has a better
learning capability. QSAN is quite suitable for fast and in-depth analysis of
the primary and secondary relationships of image and other data, which has
great potential for applications of quantum computer vision from the
perspective of enhancing the information extraction ability of models.
