Pattern Recognition In Non-Kolmogorovian Structures

We present a generalization of the problem of pattern recognition to arbitrary probabilistic models. This version deals with the problem of recognizing an individual pattern among a family of different species or classes of objects which obey probabilistic laws which do not comply with Kolmogorov's axioms. We show that such a scenario accommodates many important examples, and in particular, we provide a rigorous definition of the classical and the quantum pattern recognition problems, respectively. Our framework allows for the introduction of non-trivial correlations (as entanglement or discord) between the different species involved, opening the door to a new way of harnessing these physical resources for solving pattern recognition problems. Finally, we present some examples and discuss the computational complexity of the quantum pattern recognition problem, showing that the most important quantum computation algorithms can be described as non-Kolmogorovian pattern recognition problems

Pattern recognition on a quantum computer

By means of a simple example it is demonstrated that the task of finding and identifying certain patterns in an otherwise (macroscopically) unstructured picture (data set) can be accomplished efficiently by a quantum computer. Employing the powerful tool of the quantum Fourier transform the proposed quantum algorithm exhibits an exponential speed-up in comparison with its classical counterpart. The digital representation also results in a significantly higher accuracy than the method of optical filtering. PACS: 03.67.Lx, 03.67.-a, 42.30.Sy, 89.70.+c.Comment: 6 pages RevTeX, 1 figure, several correction

Pattern recognition on a quantum computer

By means of a simple example it is demonstrated that the task of finding and identifying certain patterns in an otherwise (macroscopically) unstructured picture (data set) can be accomplished efficiently by a quantum computer. Employing the powerful tool of the quantum Fourier transform the proposed quantum algorithm exhibits an exponential speed-up in comparison with its classical counterpart. The digital representation also results in a significantly higher accuracy than the method of optical filtering. PACS: 03.67.Lx, 03.67.-a, 42.30.Sy, 89.70.+c.Comment: 6 pages RevTeX, 1 figure, several correction

Quantum Face Recognition Protocol with Ghost Imaging

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.BERC.2018-2021 Severo Ochoa.SEV-2017-071