Dynamic developments of science and technology have demanded necessity of interdisciplinary approach and appearence of novel scientific disciplines. In this respect, face recognition using quantum mechanical methods of unitary vector spaces, represents very interesting field due to possible applications in the field of quantum informatics. Thus traditional quantum mechanical methods widely applied to microsystems during the past century, are now succesfully extrapolated in macroscopic information framework as well.
Dynamic developments of science and technology have demanded necessity of interdisciplinary approach and appearence of novel scientific disciplines. In this respect, face recognition using quantum mechanical methods of unitary vector spaces, represents very interesting field due to possible applications in the field of quantum informatics. Thus traditional quantum mechanical methods widely applied to microsystems during the past century, are now succesfully extrapolated in macroscopic information framework as well.
In this research, a new technique for measuring facial recognition face similarity based on quantum computing (oracle) has been proposed. This technique is transforming the oracle of Grover's search algorithm into correlation oracle with feeding image. The implementation is carried out on a classical computer with Matlab. The improvement in this work came from combination between quantum Grover's algorithm and classical face recognition algorithm where, the number of steps require in Grover's to find target image is which is exponential improvement as compared with steps for classical case. This reduction of steps reduces the time required to process and the complexity and enables us to increase the amount of information to be processed. The quantum phenomena such as superposition, entanglement and vector space are formulated to represent quantum information and quantum processes on the classical computer. Public available AT and T Laboratories in university of Cambridge database are tested on the proposed algorithm.
Hilbert spaces provide the fundamental mathematical framework for describing quantum mechanical systems. Their structure, characterized by an inner product and completeness, allows for the representation of quantum states as vectors and physical observables as self-adjoint operators. Key quantum phenomena such as superposition and entanglement find natural expression within this formalism. Superposition, where a quantum system can exist in multiple states simultaneously, is represented by linear combinations of basis vectors in the Hilbert space. Entanglement, a non-classical correlation between quantum systems, is described by non-separable state vectors in a tensor product of Hilbert spaces. These concepts are pivotal in quantum computing, where the unit of information, the qubit, is a two-level quantum system whose state is a vector in a two-dimensional complex Hilbert space (ℂ²). Quantum gates, which perform operations on qubits, are represented by unitary operators acting on these state vectors. The power of quantum computation, particularly in algorithms like Shor's or Grover's, stems from the ability to exploit superposition and entanglement, processes intrinsically described within the Hilbert space framework. Thus, a thorough understanding of Hilbert spaces is indispensable for grasping the principles of quantum mechanics and for advancing the field of quantum information and computation [1, 7]. The transition from classical bits to quantum qubits, and from classical logic gates to quantum unitary operations, is entirely predicated on the mathematical properties endowed by Hilbert spaces.
Hilbert spaces provide the fundamental mathematical framework for describing quantum mechanical systems. Their structure, characterized by an inner product and completeness, allows for the representation of quantum states as vectors and physical observables as self-adjoint operators. Key quantum phenomena such as superposition and entanglement find natural expression within this formalism. Superposition, where a quantum system can exist in multiple states simultaneously, is represented by linear combinations of basis vectors in the Hilbert space. Entanglement, a non-classical correlation between quantum systems, is described by non-separable state vectors in a tensor product of Hilbert spaces. These concepts are pivotal in quantum computing, where the unit of information, the qubit, is a two-level quantum system whose state is a vector in a two-dimensional complex Hilbert space (ℂ²). Quantum gates, which perform operations on qubits, are represented by unitary operators acting on these state vectors. The power of quantum computation, particularly in algorithms like Shor's or Grover's, stems from the ability to exploit superposition and entanglement, processes intrinsically described within the Hilbert space framework. Thus, a thorough understanding of Hilbert spaces is indispensable for grasping the principles of quantum mechanics and for advancing the field of quantum information and computation [1, 7]. The transition from classical bits to quantum qubits, and from classical logic gates to quantum unitary operations, is entirely predicated on the mathematical properties endowed by Hilbert spaces.
Face recognition systems are widely used for biometric authentication but face two major problems. First, processing high-resolution images and large databases requires extensive computational time. Second, emerging quantum computers threaten to break the encryption methods that protect stored facial templates. Quantum computers will soon be able to decrypt current security systems, putting biometric data at permanent risk since facial features cannot be changed like passwords. This paper presents a solution that uses quantum computing to speed up face recognition while adding quantum-resistant security. It applies quantum principal component analysis (QPCA) and the SWAP test to reduce the computational complexity and implement lattice-based cryptography, which quantum computers cannot break. Experimental evaluation demonstrates a significant overall speedup with improved accuracy. The proposed framework achieves a significant improvement in performance, provides 125-bit security against quantum attacks and compresses the data storage requirements significantly. These results demonstrate that quantum-enhanced face recognition can solve both the speed and security challenges facing current biometric systems, making it practical for real-world deployment as quantum technology advances.
Background. Quantum computing is a technology that has great potential to revolutionize various scientific disciplines with its extraordinary computing capabilities. Multiple fields, such as cryptography, optimization, and complex simulation, have begun to explore quantum computing applications. However, a deep understanding of how these technologies can be used effectively in cross-disciplinary informatics still needs to be improved. Purpose. This research aims to develop a theoretical framework that explains the role of quantum computing in advancing interdisciplinary informatics. The main focus is to identify potential applications and challenges faced in applying this technology in various fields and evaluate its impact on the development of science and technology. Method. This research uses a qualitative approach, a literature review, and theoretical analysis methods. Data were collected from various academic sources, including journals, books, and conferences relevant to quantum computing and interdisciplinary informatics. Analysis was conducted to identify patterns, themes, and relationships between quantum computing and cross-disciplinary applications. Results. The research results show that quantum computing has the potential to solve complex computational problems in various fields, including cryptography, optimization, and physical simulation. Quantum computing can provide more efficient and faster solutions than classical computing. However, the research also identified several significant challenges, such as the need for quantum-specific algorithms and sophisticated technological infrastructure. Conclusion. Quantum computing is essential in advancing interdisciplinary informatics by providing superior computing capabilities. The theoretical framework developed in this research can be used to further study and develop quantum computing applications in various fields. The identified challenges require special attention to ensure effective and efficient implementation of this technology.
This article presents the most common image recognition algorithms implemented for a quantum neural network, consider them and draw a conclusion which algorithm is preferable to use in this or that case. Currently, there is a rapid increase in interest in quantum algorithms for pattern recognition, since the first quantum computers have already appeared. This is because the use of quantum algorithms since the advent of quantum computers has allowed an exponential increase in the speed of solving NP-complete problems. Paper decide the some problems of finding and creation of approaches for the functioning of quantum calculation processes and prototypes of quantum devices in the framework of quantum image processing. A method for storing and processing image components in quantum systems is proposed, which potentially will give a significant step toward in efficiency in comparison with existing classical ones and provide a solution to a number of complex problems.