Quantum face recognition protocol with ghost imaging

Abstract

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, and quantum independent component analysis. 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 $$O(N\,\log N)$$ O ( N log 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.

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

“…Where, 𝜌 denotes the input state, 𝐸 𝑖 represents Kraus operators [66], and the sum spans all conceivable outcomes. Kraus operators are versatile in various qFE techniques like qPCA [67], quantum independent component analysis (qICA) [68], and quantum non-negative matrix factorization (qNMF) [69]. Additionally, qPCA, a quantum algorithm facilitating the extraction of PCs from datasets, relies on eigenvalue decomposition to identify directions of maximum variance.…”