Over the past few years, quantum computing has been evolving, raising concerns about how long our existing encryption methods will remain reliable. Given this, random number generators that are both high entropy and secure from quantum threats are needed. In this research, we present a hybrid random number generator system designed to produce quantum-resistant cryptographic keys to mitigate the risk of future quantum adversaries. This HRNG design integrates true entropy of a quantum random number generator (QRNG) and combines it with the high-speed performance of a ChaCha20-based pseudo random number generator (PRNG). This approach successfully balances the speed of classical algorithms with quantum unpredictability. When integrated with symmetric (AES, Blowfish, Triple DES, IDEA) and asymmetric (RSA, ECC) encryption algorithms, it maintained high-speed encryption performance without any difficulties from the limitations of physical QRNG. By avoiding hardware constraints and performance bottlenecks, the HRNG provides a deployable entropy solution that easily integrates into existing cryptographic systems.
Over the past few years, quantum computing has been evolving, raising concerns about how long our existing encryption methods will remain reliable. Given this, random number generators that are both high entropy and secure from quantum threats are needed. In this research, we present a hybrid random number generator system designed to produce quantum-resistant cryptographic keys to mitigate the risk of future quantum adversaries. This HRNG design integrates true entropy of a quantum random number generator (QRNG) and combines it with the high-speed performance of a ChaCha20-based pseudo random number generator (PRNG). This approach successfully balances the speed of classical algorithms with quantum unpredictability. When integrated with symmetric (AES, Blowfish, Triple DES, IDEA) and asymmetric (RSA, ECC) encryption algorithms, it maintained high-speed encryption performance without any difficulties from the limitations of physical QRNG. By avoiding hardware constraints and performance bottlenecks, the HRNG provides a deployable entropy solution that easily integrates into existing cryptographic systems.
One of the solutions to the problem of secure communication is the Quantum Random Number Generators (QRNGs) which generate true randomness based on quantum mechanical principles.QRNGs are different from conventional pseudo random number generator in that they use quantum uncertainty to generate outputs which are very unpredictable.This work attempts to examine different QRNG architectures, study the possible integration of cryptographic frameworks with these architectures and highlight potential vulnerabilities in the integration of cryptographic frameworks with QRNG architectures.Post-processing techniques, i.e.Zero-Phase Component Analysis (ZPCA) and neural network based techniques are also studied to increase entropy and suppress environmental noise in QRNG systems.Key findings show that system jitter models based QRNG architectures are more scalable and less expensive for use in real world applications.Additionally, incorporating cryptographic protocols with the QRNGs endows them with the ability to resist quantum threats, and hence, provide better key distribution in the secure IoT communication frameworks.However, as security problems such as side channel attacks and hardware manipulation are still big issues.It also points out some of the emerging research trends including hybrid QRNG architectures, i.e., QRNG with classical PRNG to increase the entropy rate.As such, the proposed methodologies attempt to enhance QRNG scalability, security, and energy efficiency in cloud infrastructures, in mobile systems and in critical security environments.The advancement of these technologies will assume a crucial function in the advancement of quantum-safe frames for current advanced computerized foundation.
This paper explores the design of cryptographic algorithms leveraging the inherent randomness of quantum random number generators (QRNGs). Traditional cryptographic algorithms often rely on pseudo-random number generators (PRNGs), which are deterministic and therefore vulnerable to attacks if their internal state is compromised. QRNGs, on the other hand, derive their randomness from quantum mechanical phenomena, offering a fundamentally unpredictable source of numbers. This research investigates the application of QRNGs to enhance cryptographic security, focusing on constructing algorithms that exploit this unique property. The core claim is that utilizing QRNG characteristics allows for the design of more secure cryptographic algorithms. The proposed mechanism involves capitalizing on the non-deterministic nature of QRNGs to build algorithms with heightened security levels. This approach presents a novel security guarantee method by incorporating true randomness into cryptographic processes. We examine specific algorithm designs and analyze their security implications compared to traditional PRNG-based approaches. The paper concludes with a discussion on the challenges and future directions of utilizing QRNGs in cryptography.
Random Number Generators (RNGs) are used in various applications, including cryptography, scientific simulations, gaming, and virtual reality. The quality of these random numbers is crucial, especially for computational approaches that rely on unpredictability to produce accurate results. In this thesis, we perform a comprehensive assessment of the quality of a novel Quantum Random Number Generator (QRNG) based on single photon. QRNGs leverage quantum mechanical properties to produce truly random numbers. This ensures high entropy, unpredictability, and security, making QRNGs particularly useful in fields that require high-quality randomness.We evaluate the statistical properties of strings generated by our QRNG and then its practical application by comparing it to traditional Pseudo-Random Number Generators (PRNGs) in Monte Carlo Integration (MCI), a technique that estimates integrals using random sampling. In particular, we use two statistical randomness test suits, namely NIST and Dieharder, to assess the statistical properties of our QRNG. All tests within these tests were passed, confirming that the corresponding QRNG achieves true randomness with sufficient high entropy. Next, we use our QRNG-generated numbers to seed Monte Carlo Integration (MCI) and compare it against results obtained using classical PRNG. The QRNG-based MCI demonstrated more accurate and stable numerical estimates, showing lower error rates and improved convergence compared to PRNG-based simulations.These findings highlight the advantages of QRNGs in enhancing numerical accuracy and computational security. While QRNGs may have slower generation rates than PRNGs, their superior randomness quality allows for more reliable Monte Carlo-based simulations, suggesting that fewer samples may be needed to achieve the same level of accuracy compared to classical PRNGs. This research underscores the potential of QRNG-powered numerical methods, paving the way for their integration into more advanced applications, including scientific computing, financial modeling, and cryptographic security.
The quality of random numbers directly determines the security of a cryptographic system. Traditional pseudo-random number generators (PRNGs) rely on a limited-length seed. Once the entropy is insufficient or the seed is attacked, although the generated sequence may appear to be close to true randomness in appearance, its actual security will significantly decline. To address this issue, this paper proposes an encryption method that combines quantum key technology with commercial encryption algorithms and applies it to an intelligent file cabinet security system. This method utilizes a quantum random number generator to create high-entropy encryption keys and integrates SM2, SM3, and SM4 block encryption algorithms to construct an efficient and reliable file encryption, transmission, and management solution. It ensures complete ciphertext protection, authentication, and data integrity throughout the file storage and retrieval process. The experimental results show that QRNG outperforms common PRNGs in terms of statistical distribution, sequence independence, and resistance to prediction attacks, achieving higher entropy source quality and lower attack surface risk. Further experiments with weak seeds and insufficient early entropy verified the unique advantages of QRNG in avoiding security degradation. Moreover, the KL difference between the encrypted ciphertext and the plaintext is approximately 1.2 times that of RSA + AES. These results indicate that this method effectively enhances the confidentiality and integrity of file exchange and has practical application and innovation potential.
The success of cybersecurity security depends on cryptographic protocols because they shield sensitive information as well as communication channels and serve to authenticate procedures. These protocols derive their foundation from producing robust random numbers because these numbers serve as the basis for encryption key creation and digital signature generation along with secure access protocols. Random number generators used traditionally such as PRNGs and HRNGs suffer from determinism, and predictability flaws combined with the risk of outside tampering thus affecting their reliability. The weaknesses in cryptographic security become dangerous because of the rising quantum computer threats that would break current encryption methods. Quantum Random Number Generators (QRNGs) introduce revolutionary changes through the quantum mechanics properties of quantum superposition and photon polarization and vacuum fluctuations for truly random number generation. The use of QRNGs brings high-security benefits by giving algorithms unpredictable outputs that hackers cannot predict nor alter without being detected. Implementing QRNGs in cybersecurity networks enables better encryption algorithms as well as securely protected communication systems with strong resistance against contemporary cyber attacks. This article outlines the advantages of QRNGs over traditional RNGs, applications in cryptographic systems, and how they can protect against threats from emerging quantum computing technologies. It also covers the challenges in implementing QRNGs, including hardware complexity, cost, and integration into existing security infrastructures. As cyber threats continue to evolve, QRNGs represent an important milestone in ensuring robust and future-proof cryptographic security
Cryptographic random number generation is critical for any quantum-safe encryption. Based on the natural uncertainty of some quantum processes, a variety of quantum random number generators, or QRNGs, have been created with physical quantum processes. These typically generate random numbers with good unpredictable randomness. Of course, physical QRNGs are costic and require physical integrations with computing systems. This paper proposes a pseudo quantum random number generator with a quantum algorithm called a quantum permutation pad, or QPP, leveraging the high entropy of quantum permutation space for its bijective transformation. Unlike Boolean algebra, where the size of information space is 2nfor an n-bit system, an n-bit quantum permutation space consists of 2n! quantum permutation matrices, representing all quantum permutation gates over an n-bit computational basis. This permutation space holds an equivalent Shannon information entropy of log2(2n!). A QPP can be used to create a pseudo-QRNG or pQRNG capable of integration with any classical computing system, or directly with any application, for good-quality deterministic random number generation. Using a QPP pad with 64 8-bit permuation matrices, a pQRNG holds 107,776 bits of entropy for pseudo-random number generation, compared with 4,096 bits of entropy in Linux /dev/random. It can be used as a deterministic PRNG or as an entropy booster for other PRNGs. It can also be used as a whitening algorithm for any hardware random number generator, including QRNGs, without discarding physical bias bits.