Neural Network Random Number Generator Using GAN Framework
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Solution Overview
Problem
Conventional random number generators (RNGs) lack sufficient entropy and scalability, and are not easily upgradable, while quantum-based RNGs provide high entropy but are expensive and limited in throughput.
Innovation Solution
A system and method using a generative adversarial network (GAN) framework to train a neural network to generate random numbers, leveraging a quantum random number generator (QRNG) as an entropy source, with evaluation modules to assess and enhance randomness, allowing for scalable and cost-effective high-entropy random number generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum-based RNG is used, then entropy is improved, but cost and device complexity increase
Solution Approach 1:
The patent creates a copy of the quantum random number generator's output distribution using a neural network. Instead of directly using the expensive QRNG for all purposes, the system trains a neural network to replicate the statistical properties of QRNG output, allowing software-based generation that mimics quantum randomness without requiring physical quantum hardware for all operations.
Solution Approach 2:
The patent replaces the expensive quantum hardware with a software-based neural network model that can be implemented at lower cost. The neural network serves as a disposable computational model that can be trained and deployed without the high infrastructure costs associated with quantum hardware, making the solution more economically viable.
2Measurement precision
If quantum-based RNG is used, then entropy is improved, but throughput is reduced
Solution Approach 1:
The neural network learns to replicate the statistical distribution of quantum random numbers, allowing the system to generate high-entropy random numbers through computational processes that are faster than physical quantum measurement. The network can generate multiple random numbers efficiently by processing through its computational graph without the speed limitations of quantum hardware.
Solution Approach 2:
The patent substitutes the physical quantum mechanical process with a computational neural network model. Instead of relying on quantum physical processes that have inherent speed limits, the system uses software-based neural network inference that can operate at much higher throughput rates while maintaining the statistical properties of quantum randomness.
3Device complexity
If conventional RNG is used, then device complexity is reduced, but entropy is insufficient
Solution Approach 1:
The patent employs a feedback mechanism where the neural network's performance is evaluated against the actual quantum random number distribution, and the network parameters are adjusted accordingly. This feedback loop allows the system to improve the quality of generated random numbers over time, achieving high entropy while maintaining relatively simple device architecture.
Solution Approach 2:
The system changes the parameters of the neural network model during training to optimize its ability to replicate quantum random number statistics. By adjusting network architecture parameters, training data characteristics, and optimization hyperparameters, the system achieves sufficient entropy without requiring complex hardware infrastructure.
4Productivity
If GAN framework is used, then throughput is improved, but device complexity increases
Solution Approach 1:
The patent uses a simplified copy of the GAN framework where only the essential components are implemented. Rather than using a full-blown GAN with complex generator and discriminator networks, the system employs a neural network that is trained to replicate quantum random number statistics, reducing the overall complexity while maintaining high throughput capability.
Data Source
AI summary
A method for training a neural network adapted to generate random numbers and the system thereof is disclosed. The neural network is configured to generate random numbers identical to the distribution of a quantum random number generator based on the received random noise input using a generative adversarial network framework. The method includes feeding random noise to the generator to generate a noisy output. The noisy output is fed to the discriminator along with a real time output of a quantum random number generator to the discriminator. Further, the discriminator is trained to learn the entropy of the real-time output and distinguish it from the noisy output based on the learned entropy. Finally, the output of the discriminator is fed as feedback to the generator.