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 (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(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.

Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?

“…The envisaged BCI chip can be implanted on the interior surface of the skull to monitor in real-time UPE signals emanating from the cortex surface. The proposed chip is not only useful for BCI technology but also it can be used as a photonic sensor for imaging, spectroscopy and sensitive measurements at low light-levels in several applications from biological UPE to quantum optical processing [89]. Although our proposed technology is, admittedly, at the level of conjecture, requiring comprehensive tests and investigations for verification, we still envision complementary features as well as certain advantages over established technologies, including ECoG.…”

Interferometric imaging of amplitude and phase of spatial biphoton states

“…Coincidence imaging can be achieved with modern EMCCD cameras [31,32], SPAD arrays [33][34][35] or time stamping cameras [36]. These technologies are commonly exploited in quantum imaging, such as ghost imaging experiments [37][38][39][40] or quantum superresolution [41][42][43], as well as for fundamental applications, such as characterizing two-photon correlations [32,44], imaging of high-dimensional Hong-Ou-Mandel interference [45][46][47][48], and visualization of the violation of Bell inequalities [49]. Holography techniques have been recently proposed in the context of quantum imaging [50,51]; demonstrating the phaseshifting digital holography in a coincidence imaging regime using polarization entanglement [50], and exploiting induced coherence, i.e.…”