  Face recognition is one of the most ubiquitous examples of pattern
recognition in machine learning, with numerous applications in security, access
control, and law enforcement, among many others. Pattern recognition with
classical algorithms requires significant computational resources, especially
when dealing with high-resolution images in an extensive database. Quantum
algorithms have been shown to improve the efficiency and speed of many
computational tasks, and as such, they could also potentially improve the
complexity of the face recognition process. Here, we propose a quantum machine
learning algorithm for pattern recognition based on quantum principal component
analysis (QPCA), and quantum independent component analysis (QICA). A novel
quantum algorithm for finding dissimilarity in the faces based on the
computation of trace and determinant of a matrix (image) is also proposed. The
overall complexity of our pattern recognition algorithm is O(Nlog N) -- $N$ is
the image dimension. As an input to these pattern recognition algorithms, we
consider experimental images obtained from quantum imaging techniques with
correlated photons, e.g. "interaction-free" imaging or "ghost" imaging.
Interfacing these imaging techniques with our quantum pattern recognition
processor provides input images that possess a better signal-to-noise ratio,
lower exposures, and higher resolution, thus speeding up the machine learning
process further. Our fully quantum pattern recognition system with quantum
algorithm and quantum inputs promises a much-improved image acquisition and
identification system with potential applications extending beyond face
recognition, e.g., in medical imaging for diagnosing sensitive tissues or
biology for protein identification.
