This project explores the integration of Quantum Random Number Generators (QRNGs) into spacecraft navigation systems.
Traditional navigation algorithms rely on pseudo-random processes that are deterministic in nature. Quantum random number generators, however, derive randomness from fundamental quantum mechanical phenomena, providing true randomness.
The objective of this project is to investigate whether QRNG-based randomness can improve robustness, uncertainty modeling, and stochastic navigation algorithms used in spacecraft guidance and deep space navigation.
Space navigation systems must operate under conditions of uncertainty such as:
- sensor noise
- signal delays
- environmental disturbances
- unpredictable system dynamics
True quantum randomness may help enhance stochastic modeling approaches used in navigation algorithms.
This research investigates how QRNG-based randomness can be incorporated into simulation pipelines for spacecraft navigation.
The key goals of this project are:
- Explore the role of quantum randomness in navigation algorithms
- Simulate navigation processes using QRNG-generated randomness
- Compare deterministic and quantum-random navigation strategies
- Analyze trajectory estimation under stochastic perturbations
Navigation pipeline used in this project: QRNG Source
↓
Randomness Processing
↓
Navigation Simulation
↓
Trajectory Estimation
↓
Performance Analysis
The QRNG component provides high-quality randomness that can be used in stochastic simulation or probabilistic navigation algorithms.
qrng-spacecraft-navigation │ ├── src/ # Core implementation ├── simulations/ # Navigation simulations ├── experiments/ # Experimental scripts ├── results/ # Generated outputs and plots ├── notebooks/ # Analysis notebooks └── README.md
(The exact structure may vary depending on simulation scripts.)
Clone the repository: git clone https://github.com/shrashtimittal/qrng-spacecraft-navigation.git
cd qrng-spacecraft-navigation
Install required dependencies: pip install -r requirements.txt
Example command: python simulate_navigation.py
This will run the navigation simulation using QRNG-generated randomness.
Outputs typically include:
- navigation error statistics
- trajectory deviation plots
- randomness distribution analysis
Potential applications include:
- Deep space navigation
- autonomous spacecraft guidance
- stochastic trajectory estimation
- resilient navigation algorithms
Future extensions of this project may include:
- integration with real QRNG hardware
- quantum-enhanced Kalman filters
- hybrid classical–quantum navigation models
- space mission navigation simulations
- Quantum Random Number Generators – Herrero-Collantes & Garcia-Escartin
- Principles of Spacecraft Navigation – NASA JPL
- Quantum Information Science and Technology applications in aerospace
Shrashti Mittal
AI • Aerospace Systems • Quantum Computing