Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Entropic uncertainty in quantum-state cryptography : A mathematical framework for quantum-resilient encryption

View through CrossRef
In the escalating race between cryptographic security and quantum computing capabilities, the need for robust encryption methodologies that can withstand the prowess of quantum algorithms is more pressing than ever. This paper introduces a novel cryptographic framework, grounded in the principles of quantum mechanics and the entropic uncertainty principle, to forge a path towards quantum-resilient encryption. At the heart of this approach lies the integration of the entropic uncertainty inherent in quantum states, a fundamental aspect often overlooked in traditional cryptographic strategies. By harnessing this intrinsic uncertainty of quantum mechanics, we propose a mathematical framework that not only challenges the conventional paradigms of encryption but also sets a new benchmark for security in the quantum computing era. The paper delves into the theoretical underpinnings of quantum mechanics relevant to cryptography, with a particular focus on the entropic uncertainty principle. This principle, which posits a natural limit on the precision with which certain pairs of physical properties can be known, serves as the cornerstone of our proposed encryption method. We meticulously develop and outline a mathematical model that leverages this principle, ensuring that the encrypted information remains secure against the formidable computational capabilities of quantum algorithms. We contrast our approach with existing cryptographic methods, highlighting the enhanced security features offered by the entropic uncertainty-based model. The findings underscore the potential of this framework to serve as a resilient encryption mechanism in a landscape increasingly dominated by quantum computing technologies. This research paves the way for a new era of encryption, one that embraces the uncertainty of quantum mechanics as its shield against the threats posed by quantum computing.
Title: Entropic uncertainty in quantum-state cryptography : A mathematical framework for quantum-resilient encryption
Description:
In the escalating race between cryptographic security and quantum computing capabilities, the need for robust encryption methodologies that can withstand the prowess of quantum algorithms is more pressing than ever.
This paper introduces a novel cryptographic framework, grounded in the principles of quantum mechanics and the entropic uncertainty principle, to forge a path towards quantum-resilient encryption.
At the heart of this approach lies the integration of the entropic uncertainty inherent in quantum states, a fundamental aspect often overlooked in traditional cryptographic strategies.
By harnessing this intrinsic uncertainty of quantum mechanics, we propose a mathematical framework that not only challenges the conventional paradigms of encryption but also sets a new benchmark for security in the quantum computing era.
The paper delves into the theoretical underpinnings of quantum mechanics relevant to cryptography, with a particular focus on the entropic uncertainty principle.
This principle, which posits a natural limit on the precision with which certain pairs of physical properties can be known, serves as the cornerstone of our proposed encryption method.
We meticulously develop and outline a mathematical model that leverages this principle, ensuring that the encrypted information remains secure against the formidable computational capabilities of quantum algorithms.
We contrast our approach with existing cryptographic methods, highlighting the enhanced security features offered by the entropic uncertainty-based model.
The findings underscore the potential of this framework to serve as a resilient encryption mechanism in a landscape increasingly dominated by quantum computing technologies.
This research paves the way for a new era of encryption, one that embraces the uncertainty of quantum mechanics as its shield against the threats posed by quantum computing.

Related Results

Advanced frameworks for fraud detection leveraging quantum machine learning and data science in fintech ecosystems
Advanced frameworks for fraud detection leveraging quantum machine learning and data science in fintech ecosystems
The rapid expansion of the fintech sector has brought with it an increasing demand for robust and sophisticated fraud detection systems capable of managing large volumes of financi...
Reserves Uncertainty Calculation Accounting for Parameter Uncertainty
Reserves Uncertainty Calculation Accounting for Parameter Uncertainty
Abstract An important goal of geostatistical modeling is to assess output uncertainty after processing realizations through a transfer function, in particular, to...
A Survey about Post Quantum Cryptography Methods
A Survey about Post Quantum Cryptography Methods
Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography...
Advancements in Quantum Computing and Information Science
Advancements in Quantum Computing and Information Science
Abstract: The chapter "Advancements in Quantum Computing and Information Science" explores the fundamental principles, historical development, and modern applications of quantum co...
Revolutionizing multimodal healthcare diagnosis, treatment pathways, and prognostic analytics through quantum neural networks
Revolutionizing multimodal healthcare diagnosis, treatment pathways, and prognostic analytics through quantum neural networks
The advent of quantum computing has introduced significant potential to revolutionize healthcare through quantum neural networks (QNNs), offering unprecedented capabilities in proc...
Quantum information outside quantum information
Quantum information outside quantum information
Quantum theory, as counter-intuitive as a theory can get, has turned out to make predictions of the physical world that match observations so precisely that it has been described a...
Integrating quantum neural networks with machine learning algorithms for optimizing healthcare diagnostics and treatment outcomes
Integrating quantum neural networks with machine learning algorithms for optimizing healthcare diagnostics and treatment outcomes
The rapid advancements in artificial intelligence (AI) and quantum computing have catalyzed an unprecedented shift in the methodologies utilized for healthcare diagnostics and trea...

Back to Top