معرفی شرکت ها
ccvm-simulators-0.1.2
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Solve continuous non-convex optimization problems with CCVM architectures and simulators
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ccvm-simulators-0.1.2 |
| نام | ccvm-simulators |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | 1QBit |
| ایمیل نویسنده | support@1qbit.com |
| آدرس صفحه اصلی | https://github.com/1QB-Information-Technologies/ccvm |
| آدرس اینترنتی | https://pypi.org/project/ccvm-simulators/ |
| مجوز | GNU Affero General Public License v3.0 |
# Coherent Continous-Variable Machine Simulators
[](https://www.gnu.org/licenses/agpl-3.0)
[](http://1qbit.com/)
[](https://arxiv.org/abs/2209.04415)
[](https://urban-chainsaw-9k39nm4.pages.github.io/index.html)
This software package includes solvers for continuous optimization problems. The solvers are simulators of coherent continuous-variable machines (CCVM), which are novel coherent network computing architectures based on NTT Research’s coherent Ising machines (CIM). In CCVMs, the physical properties of optical pulses (e.g., mean-field quadrature amplitudes and phase) represent the continuous variables of a given optimization problem. Various features of CCVMs can be used along with programming techniques to implement the constraints imposed by such an optimization problem. Included are methods for solving box-constrained quadratic programming (BoxQP) problems using CCVM simulators by mapping the variables of the problems into the random variables of CCVMs.
## Table of Contents
0. [Requirements](#requirements)
1. [Quickstart](#quickstart)
2. [Usage](#usage)
3. [Documentation](#docs)
- [BoxQP Problem Definition](ccvm/problem_classes/README.md)
- [ccvmplotlib](ccvm/ccvmplotlib/README.md)
4. [Contributing](#contributing)
5. [References](#references)
6. [License](#license)
## Requirements
- Python (supported version: 3.10)
### Supported operating systems
- macOS (Monterey, Big Sur)
- Ubuntu (20.04)
## Quickstart
1. Once you have cloned the repository, install the package using `pip`.
```
pip install ccvm-simulators
```
2. Go into `examples/` and run the demo scripts.
- `ccvm_boxqp.py`: Solve a BoxQP problem using a CCVM simulator, w/o plotting
- `ccvm_boxqp_plot.py`: Solve a BoxQP problem using a CCVM simulator, w/ time-to-solution (TTS) plotting
3. View the generated plot.
- The `ccvm_boxqp_plot.py` script solves a single problem instance, and will create an image of the resulting TTS plot in a `plots/` directory. The resulting output image, `DL-CCVM_TTS_cpu_plot.png`, will look something like this:
<p align="center">
<img src="ccvm/ccvmplotlib/images/single_instance_TTS_plot.png" width="250" >
</p>
### Extending the Example
4. Plotting larger datasets
- The `ccvm_boxqp_plot.py` script is a toy example that plots the TTS for only a single problem instance.
- It can be extended to solve multiple problems over a range of problem sizes.
- When solution metadata is saved for multiple problems, the graph becomes more informative, for example:
<p align="center">
<img src="ccvm/ccvmplotlib/images/tts_plot_example.png" width="250" >
</p>
5. Other types of plots
- `ccvmplotlib` can also plot the success probability data, for example:
<p align="center">
<img src="ccvm/ccvmplotlib/images/success_prob_plot_example.png" width="250">
</p>
## Usage
### Solving a BoxQP Problem
##### 1. Import Modules
```python
from ccvm_simulators.problem_classes.boxqp import ProblemInstance
from ccvm_simulators.solvers import DLSolver
```
##### 2. Define a Solver
```python
solver = DLSolver(device="cpu", batch_size=100) # or "cuda"
solver.parameter_key = {
20: {"pump": 2.0, "lr": 0.005, "iterations": 15000, "noise_ratio": 10},
}
```
##### 3. Load a Problem Instance
```python
boxqp_instance = ProblemInstance(
instance_type="test",
file_path="./examples/test_instances/test020-100-10.in",
device=solver.device,
)
```
##### 4. Scale the Coefficients
The BoxQP problem matrix Q and vector V are normalized to obtain a uniform
performance across different problem sizes and densities. The ideal range depends on the
solver. For best results, Q should be passed to the solver's `get_scaling_factor()`
method to determine the best scaling value for the problem–solver combination.
```python
boxqp_instance.scale_coefs(solver.get_scaling_factor(boxqp_instance.q_matrix))
```
##### 5. Solve the Problem Instance
```python
solution = solver.solve(
instance=boxqp_instance,
post_processor=None,
)
print(f"The best known solution to this problem is {solution.optimal_value}")
# The best known solution to this problem is 799.560976
print(f"The best objective value found by the solver was {solution.best_objective_value}")
# The best objective value found by the solver was 798.1630859375
print(f"The solving process took {solution.solve_time} seconds")
# The solving process took 8.949262142181396 seconds
```
## Documentation
The package documentation can be found [here](https://urban-chainsaw-9k39nm4.pages.github.io/index.html).
* TODO: Update with public link
Additional links:
- Problem definition: [BoxQP problem class](ccvm/problem_classes/README.md)
- Plotting library: [ccvmplotlib](ccvm/ccvmplotlib/README.md)
## Contributing
We appreciate your pull requests and welcome opportunities to discuss new ideas. Check out our [contribution guide](CONTRIBUTING.md) and feel free to improve the `ccvm_simulators` package. For major changes, please open an issue first to discuss any suggestions for changes you might have.
Thank you for considering making a contribution to our project! We appreciate your help and support.
## References
This repository contains architectures and simulators presented in the paper ["Non-convex Quadratic Programming Using Coherent Optical Networks"](https://arxiv.org/abs/2209.04415) by Farhad Khosravi, Ugur Yildiz, Artur Scherer, and Pooya Ronagh.
## License
[APGLv3](https://github.com/1QB-Information-Technologies/ccvm/blob/main/LICENSE)
نیازمندی
| مقدار | نام |
|---|---|
| ==66.0.0 | setuptools |
| ==0.18.3 | future |
| ==3.6.3 | matplotlib |
| ==1.24.1 | numpy |
| ==1.5.2 | pandas |
| ==7.2.0 | pytest |
| ==1.10.0 | scipy |
| ==1.13.1 | torch |
| ==1.5.2 | json-stream |
| ==4.64.1 | tqdm |
| ==1.7.7 | xmlrunner |
نحوه نصب
نصب پکیج whl ccvm-simulators-0.1.2:
pip install ccvm-simulators-0.1.2.whl
نصب پکیج tar.gz ccvm-simulators-0.1.2:
pip install ccvm-simulators-0.1.2.tar.gz