In the mid-1990s, researchers proposed methods to preserve the integrity of quantum bits—techniques that may become the key to practical quantum computing on a large scale.
The Intelligence Community, DOD, financial, and commercial entities rely on encryption to protect its most sensitive information. Public Key Encryption (PKE) method is used to encrypt information using the principle that it is difficult to factor a very large number into its primes unless one has access to a private key that was used to encrypt the information. Using this technology, decrypting a message using a 128-bit long key would take a classical supercomputer about 1 billion billion years to decrypt. However, in the mid-1990s, mathematicians Shor and Grover developed algorithms that have proved to be efficient in factoring large numbers into primes using a quantum computer. The development of quantum hardware and algorithms therefore threaten encryption protection. In 2019, Google announced that it established quantum supremacy, meaning that its quantum computer solved a problem that no classical computer could. The authors of this paper conducted a study to describe the latest encryption research and development, methods used, and the potential vulnerability due to quantum computing. The authors further established the timeframes in which pre-quantum encryption method will become vulnerable under three scenarios. All the research for this study was derived from unclassified open source materials.
Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.