yquantum-scramble is an innovative system designed to verify the outputs of quantum random number generators (QRNGs) and maintain a record of the machines that produced these outputs leading to device independence (DI-QRNGs). This project aligns with our efforts to contribute to the DoraHacks challenge, focusing on classifying QRNGs by analyzing their output characteristics.
Quantum random number generators (QRNGs) are pivotal in various applications ranging from cryptography to scientific simulations.
CHSH and NIST have high fidelity, but we should continue hardening randomness generation.
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Grade the Independent Quantum Device Randomizer using NIST Statistical tests and CHSH standards.
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Apply the scramble model classifier to see if it can accurately find a pattern within the expected random numbers.
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You are left with verifiable random seeds that pass the NIST, CHSH, and trained convolutional neural network (CNN) tests.
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The device is initialized on the blockchain for traceability and insurance from future malpractice with faulty machines.
Check it out; it's free! Minus the Quantum computer credits!!
Quantum computing is still expensive and thus needs a more significant ability to gain training data to build and improve accurate models.
We selected a simple 1D CNN to demonstrate the idea and show what a robust model can look like.
https://qdc-solution-li9h3vh.gamma.site/
Our solution comprises several critical components that collectively ensure the reliability and efficacy of quantum-generated randomness:
- Random Number Generation: We generate our own random numbers utilizing Device-Independent Quantum Random Number Generation (DI-QRNG) based on the Clauser-Horne-Shimony-Holt (CHSH) game method.
- Randomness Verification: Verification of the randomness of numbers is done using the NIST Statistical Test Suite, ensuring they meet the established randomness criteria.
- Quantum Evaluation Metrics: We compute CHSH scores and entanglement fidelities to evaluate the performance of our QRNGs.
- Machine Classification: Newly generated samples are classified according to the quantum machine of origin.
- Blockchain Logging: Each batch of random numbers is tagged with its origin device and securely logged in a blockchain.
"To set up yquantum-scramble on your local machine for development and testing purposes, follow these steps:
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Clone the repository: 'git clone https://github.com/your-organization/yquantum-scramble.git'
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Navigate to the project directory: 'cd yquantum-scramble'
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Install the required dependencies: 'pip install -r requirements.txt'"
To start using yquantum-scramble, run the following command:
'python main.py'
This script will initiate the QRNG process, perform validation checks, and log the data to the blockchain.
We welcome contributions from the community. If you wish to contribute to yquantum-scramble, please fork the repository and submit a pull request. For substantial changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.