  Self-Attention Mechanism (SAM) excels at distilling important information
from the interior of data to improve the computational efficiency of models.
Nevertheless, many Quantum Machine Learning (QML) models lack the ability to
distinguish the intrinsic connections of information like SAM, which limits
their effectiveness on massive high-dimensional quantum data. To tackle the
above issue, a Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to
combine the data representation merit of Quantum Kernel Methods (QKM) with the
efficient information extraction capability of SAM. Further, a Quantum Kernel
Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which
ingeniously incorporates the Deferred Measurement Principle (DMP) and
conditional measurement techniques to release half of quantum resources by
mid-circuit measurement, thereby bolstering both feasibility and adaptability.
Simultaneously, the Quantum Kernel Self-Attention Score (QKSAS) with an
exponentially large characterization space is spawned to accommodate more
information and determine the measurement conditions. Eventually, four QKSAN
sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary
classification on MNIST and Fashion MNIST, where the QKSAS tests and
correlation assessments between noise immunity and learning ability are
executed on the best-performing sub-model. The paramount experimental finding
is that a potential learning advantage is revealed in partial QKSAN subclasses
that acquire an impressive more than 98.05% high accuracy with very few
parameters that are much less in aggregate than classical machine learning
models. Predictably, QKSAN lays the foundation for future quantum computers to
perform machine learning on massive amounts of data while driving advances in
areas such as quantum computer vision.
