-**Description**: The CFA method, introduced by Ding et al., involves using a multiplier encoder to factorize integers on a quantum annealer.
-**Description**: The CFA method, introduced by Ding et al., involves using a custom embedding to factorize integers on a quantum annealer.
-**Code Reference**: Data was generated using the code provided in the repository linked below.
-**Repository Link**: [Multiplier Encoder by Ding et al.](https://gitlab.com/jingwen.ding/multiplier-encoder/)
-**Paper Reference**: Ding et al., arXiv:2310.17574
### 3. Modified Multiplication Table (Direct Method) (Jiang et al.)
-**Description**: This method modifies the traditional multiplication table approach, applying it directly to quantum annealing for prime factorization.
-**Description**: This method modifies the traditional multiplication table approach by splitting it into blocks for reduced complexity
-**Code Reference**: The data was generated using code available in the `JupsiFactoring/modifiedmultiplication` directory of this repository.
-**Paper Reference**: Jiang et al., "Quantum Annealing for Prime Factorization", arXiv:1804.02733
## How to Use this Repository
## Data Availability
All data generated from the aforementioned methods is available in the `data` folder of this repository. This folder is organized as follows:
-**`*_success.pckl` Files**: These files contain success probabilities for each method, grouped by problem size.
-**`ground_states.pckl`**: This file contains the percentage of samples in which the lowest energy state was found.
-**`success_rates_per_semiprime/` Folder**: This folder contains success rates for each semiprime, not grouped by problem size.
