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NishMath - #Applications
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Recent advancements in quantum computing highlight the critical mathematical foundations that underpin this emerging technology. Researchers are delving into the intricacies of quantum bits (qubits), exploring how they represent information, which is fundamentally different from classical bits, with techniques using packages like Qiskit. The mathematical framework describes qubits as existing in a superposition of states, a concept visualized through the Bloch sphere, and utilizes complex coefficients to represent the probabilities of measuring those states. Furthermore, the study of multi-qubit systems reveals phenomena such as entanglement, a critical resource that facilitates quantum computation and secure communication.
Quantum cryptography is another area benefiting from quantum mechanics, using superposition and entanglement for theoretically unbreakable security. Quantum random bit generation is also under development, with quantum systems producing truly random numbers critical for cryptography and simulations. In a different area of quantum development, a new protocol has been demonstrated on a 54-qubit system that generates long-range entanglement, highlighting the capabilities to control and manipulate quantum states in large systems, essential for scalable error-corrected quantum computing. These advancements are set against a backdrop of intensive research into mathematical models that represent how quantum phenomena differ from classical physics.
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admin@ICNAAM 2025
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The International Conference of Numerical Analysis and Applied Mathematics (ICNAAM) 2025 will feature a symposium on Statistical Modeling and Data Analysis. The event, organized by Luis M. Grilo from the University of Évora and the Research Centre for Mathematics and Applications in Portugal, aims to gather researchers from various fields with expertise in statistical models and data analysis. Academics, professionals, and students interested in these areas are encouraged to submit original, unpublished results for peer review.
Applications with real-world data are particularly welcome, spanning disciplines such as Health Sciences, Natural and Life Sciences, Social and Human Sciences, Economics, Engineering, Education, Sports, and Tourism. The conference aims to foster collaboration and knowledge sharing within the international Numerical and Applied Mathematics community. It is organized with the cooperation of the European Society of Computational Methods in Sciences and Engineering (ESCMCE).
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The intersection of mathematics and technology is proving to be a hot topic, with articles exploring how mathematical concepts underpin many aspects of data science and programming. Key areas of focus include the essential math needed for programming, highlighting the importance of Boolean algebra, number systems, and linear algebra for creating efficient and complex code. Linear algebra, specifically the application of matrices, was noted as vital for data transformations, computer vision algorithms, and machine learning, enabling tasks such as vector operations, matrix transformations, and understanding data representation.
The relationship between data science and mathematics is described as complex but crucial, with mathematical tools being the foundation of data-driven decisions. Probability and statistics are also essential, acting as lenses to understand uncertainty and derive insights, covering descriptive statistics like mean, median, mode and the application of statistical models. Computer vision also relies on math concepts, with specific applications like optical character recognition using techniques like pattern recognition and deep learning. Optimization of computer vision models is also discussed, with a focus on making models smaller and faster using techniques like pruning and quantization.
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