Welcome to the CCVM Documentation!#

Coherent Continous-Variable Machine Simulators#

License: AGPL v3 Maintainer Paper Docs

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#

  1. Requirements

  2. Quickstart

  3. Usage

  4. Documentation

  5. Contributing

  6. References

  7. 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

  1. Go into examples/ and run the demo scripts.

    • ccvm_boxqp_dl.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

    • For more demo scripts see examples/README.md

  2. 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:

Extending the Example#

  1. 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:

  1. Other types of plots

    • ccvmplotlib can also plot the success probability data, for example:

Usage#

Solving a BoxQP Problem#

1. Import Modules#
from ccvm_simulators.problem_classes.boxqp import ProblemInstance
from ccvm_simulators.solvers import DLSolver
2. Define a Solver#
solver = DLSolver(device="cpu", batch_size=100)  # or "cuda"
solver.parameter_key = {
    20: {"pump": 2.0, "dt": 0.005, "iterations": 15000, "noise_ratio": 10},
}
3. Load a Problem Instance#
boxqp_instance = ProblemInstance(
    instance_type="test",
    file_path="./examples/benchmarking_instances/single_test_instance/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.

boxqp_instance.scale_coefs(solver.get_scaling_factor(boxqp_instance.q_matrix))
5. Solve the Problem Instance#
solution = solver(
    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 effectively {solution.solve_time} seconds to solve a single instance")
# The solving process took 0.008949262142181396 seconds

Documentation#

The package documentation can be found here.

Additional links:

Contributing#

We appreciate your pull requests and welcome opportunities to discuss new ideas. Check out our contribution guide 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” by Farhad Khosravi, Ugur Yildiz, Martin Perreault, Artur Scherer, and Pooya Ronagh.

License#

APGLv3

Contents:

Indices and tables#