The real random number generation is a critical problem in computer science. The current generation methods are ei-ther too dangerous or too expensive, such as using decays of some radioactive elements. They are also hard to con-trol. By the declaration of uncertainty principles in quantum mechanics, real probabilistic events can be substituted by easier and safer processes, such as electron diffraction, photon diffraction and qubits. The key to solve the problem of Schrödinger’s cat is to identify that the atom stays in different states after and before the decay, and the result of the decay is probabilistic according to the wave packet collapse hypothesis. Same matter is able to possess different kinds of properties such as wave-particle duality due to that it can stay in various states, and which state will the matter stay is determined by the chosen set of physical quantities (or mechanical quantities). One eigenstate of a set of physical quantities can be a superposition of other eigenstates of different sets of physical quantities, and the col-lapse from a superposition to an eigenstate it contains is really random. Using this randomness, real random number can be generated more easily.
The real random number generation is a critical problem in computer science. The current generation methods are ei-ther too dangerous or too expensive, such as using decays of some radioactive elements. They are also hard to con-trol. By the declaration of uncertainty principles in quantum mechanics, real probabilistic events can be substituted by easier and safer processes, such as electron diffraction, photon diffraction and qubits. The key to solve the problem of Schrödinger’s cat is to identify that the atom stays in different states after and before the decay, and the result of the decay is probabilistic according to the wave packet collapse hypothesis. Same matter is able to possess different kinds of properties such as wave-particle duality due to that it can stay in various states, and which state will the matter stay is determined by the chosen set of physical quantities (or mechanical quantities). One eigenstate of a set of physical quantities can be a superposition of other eigenstates of different sets of physical quantities, and the col-lapse from a superposition to an eigenstate it contains is really random. Using this randomness, real random number can be generated more easily.
The real random number generation is a crucial problem in computer science.The current generation methods are either too dangerous or too expensive, such as using decay of some radioactive atoms, they are also hard to control.By the uncertainty principles in quantum mechanics, real probabilistic events can be just replaced by the collapse from a superposition to an eigenstate it consists of, such as double-slits interference of a single photon or electron, when the photon or electron hit onto the screen from a free moving state with certain momentum, is from a momentum eigenstate which is a superposition of position eigenstates to collapse onto a position eigenstate which is a superposition of position eigenstates.Schrödinger's cat paradox is not a paradox, it is an effect, there is something different happens in each half-life period before the atom decayed, to make the atom choose either decaying or not decaying in this half-life period to stay in the next half-life period, whether decay or not is purely probabilistic like an ideal dice.Same matter is able to possess different kinds of properties, because same matter can stay in different states which have different properties, such as wave-particle duality.One eigenstate of a physical quantity can be a superposition of eigenstates of another physical quantity, and the collapse from a superposition to an eigenstate it contains is purely probabilistic like an ideal dice, such probability can be used to generate real random number, and double-slits interference of single photon, with a screen that has photoelectric effect with this photon, real random number may be generated thereby for our computer and other smart things today.
The real random number generation is a crucial problem in computer science. The current generation methods are either too dangerous or too expensive, such as using decay of some radioactive atoms, they are also hard to control. By the uncertainty principles in quantum mechanics, real probabilistic events can be just replaced by the collapse from a superposition to an eigenstate it consists of, such as double-slits interference of a single photon or electron, when the photon or electron hit onto the screen from a free moving state with certain momentum, is from a momentum eigenstate which is a superposition of position eigenstates to collapse onto a position eigenstate which is a superposition of position eigenstates. Schrödinger’s cat paradox is not a paradox, it is an effect, there is something different happens in each half-life period before the atom decayed, to make the atom choose either decaying or not decaying in this half-life period to stay in the next half-life period, whether decay or not is purely probabilistic like an ideal dice. Same matter is able to possess different kinds of properties, because same matter can stay in different states which have different properties, such as wave-particle duality. One eigenstate of a physical quantity can be a superposition of eigenstates of another physical quantity, and the collapse from a superposition to an eigenstate it contains is purely probabilistic like an ideal dice, such probability can be used to generate real random number, and double-slits interference of single photon, with a screen that has photoelectric effect with this photon, real random number may be generated thereby for our computer and other smart things today.
The generation of true randomness remains a significant challenge in computer science, particularly when relying solely on classical algorithms. Many current approaches employ pseudo-random number generators (PRNGs), which, despite their computational efficiency, are inherently predictable and lack genuine randomness. This paper proposes a novel method for algorithmic randomness generation leveraging the principles of quantum walks. We demonstrate that the inherent uncertainty and superposition properties of quantum systems provide a robust mechanism for generating truly random numbers. Specifically, we outline a framework for designing quantum walks on graphs, where the evolution of the walker's state is fundamentally probabilistic and unpredictable. The core of our approach resides in exploiting the quantum walk's non-deterministic nature to produce sequences of numbers that exhibit statistical properties consistent with true randomness. The theoretical framework is presented, including a detailed description of the mathematical formalism underlying the quantum walk and the probability distribution of the walker's positions. We highlight the potential advantages of this approach, particularly in scenarios demanding high-quality randomness for cryptographic applications or simulations. The key concept is to translate a classical problem into a quantum one, where the inherent probabilistic nature of quantum mechanics offers a superior solution to the problem of generating genuine randomness.
Random Number Generators are software or hardware components that allow the production of unpredictable number sequences without any pattern or relationship between them. Various studies have been conducted with different techniques regarding RNG. In these studies, the difficulties of random number generation and the high cost negatively affect the efficiency of the developed generators. Many different methods have been used in real random number generation, and even quantum random number generators have been developed to make predictability difficult. Quantum Random Number Generators; are a tepe of generator based on the laws of Quantum physics instead of classical physics. In photonic-based RNG, random numbers are generated after various software and hardware operations by utilizing the uncertainty of photons. This study, it is aimed to develop a true random number generator using chemical reactions that have not been studied before. Data was produced by using sensors and other hardware elements together, the values produced were taken as seed values and assigned as input to the algorithm used in generating random numbers, and true random numbers were produced and these numbers were tested in detail with known test methods.
Quantum random number generation (QRNG) offers a fundamentally different approach to randomness compared to classical methods. This paper explores the design and principles of a QRNG based on harnessing the inherent uncertainty in quantum measurements. The core claim is the development of a system capable of generating truly random quantum bit sequences. This is achieved by exploiting the probabilistic nature of quantum measurement, specifically the collapse of the wave function upon observation, which yields unpredictable outcomes. The generated sequences are expected to exhibit statistical independence, a crucial property for various quantum computing applications. This work lays the groundwork for a detailed exploration of QRNG techniques, highlighting their potential impact on secure quantum communication and simulation. We will detail the theoretical underpinnings, focusing on the mathematical formulation of the process and the statistical properties of the generated random numbers.
Abstract Quantum physics can be exploited to generate true random numbers, which have important roles in many applications, especially in cryptography. Genuine randomness from the measurement of a quantum system reveals the inherent nature of quantumness—coherence, an important feature that differentiates quantum mechanics from classical physics. The generation of genuine randomness is generally considered impossible with only classical means. On the basis of the degree of trustworthiness on devices, quantum random number generators (QRNGs) can be grouped into three categories. The first category, practical QRNG, is built on fully trusted and calibrated devices and typically can generate randomness at a high speed by properly modelling the devices. The second category is self-testing QRNG, in which verifiable randomness can be generated without trusting the actual implementation. The third category, semi-self-testing QRNG, is an intermediate category that provides a tradeoff between the trustworthiness on the device and the random number generation speed.