Aqarios

QAGA - Quantum Assisted Genetic Algorithm

Description

QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms:

We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions.

This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time/iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned.

The Quantum Annealing Genetic Algorithm is used for the mutation.

Parameters

p_size: int

The population size for the genetic algorithm. Default is 40.

p_inc_num: int

The increment number for the population size. Default is 0.

p_max: int

The maximum population size. Default is -1.

pct_random_states: int

The percentage of random states in the initial population. Default is 0.

mut_rate: int

The mutation rate for the genetic algorithm. Default is 1.

rec_rate: int

The recombination rate for the genetic algorithm. Default is 1.

rec_method: str

The recombination method for the genetic algorithm. Default is "one_point_crossover".

num_sweeps: int

The number of sweeps for the simulated annealing. Default is 1000.

num_sweeps_inc_factor: int

The increment factor for the number of sweeps. Default is 1.

num_sweeps_inc_max: int

The maximum increment for the number of sweeps. Default is -1.

beta_range_type: str

The type of beta range for the simulated annealing. Default is "default".

beta_range: str

The beta range for the simulated annealing. Default is "beta_range".

target: Any, optional

The target solution for the optimization problem.

atol: int

The absolute tolerance for the optimization problem. Default is 0.

rtol: int

The relative tolerance for the optimization problem. Default is 0.

timeout: int

The timeout for the optimization problem in seconds. Default is 5.

max_iter: int

The maximum number of iterations for the optimization problem. Default is -1.

max_consistent_iters: int

The maximum number of consistent iterations for the optimization problem. Default is -1.

return_overhead: bool

Whether to return the overhead of the optimization problem. Default is False.

use_qpu: bool

Whether to use the QPU for the optimization problem. Default is True.

Available

Parameters

{
  "p_size": 40,
  "p_inc_num": 0,
  "p_max": -1,
  "pct_random_states": 0,
  "mut_rate": 1,
  "rec_rate": 1,
  "rec_method": "one_point_crossover",
  "num_sweeps": 1000,
  "num_sweeps_inc_factor": 1,
  "num_sweeps_inc_max": -1,
  "beta_range_type": "default",
  "beta_range": "beta_range",
  "target": None,
  "atol": 0,
  "rtol": 0,
  "timeout": 5,
  "max_iter": -1,
  "max_consistent_iters": -1,
  "return_overhead": False,
  "use_qpu": True
}

QPU Provider

dwave

QAGAMP - Quantum Assisted Genetic Algorithm Multiprocessing

Description

QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms while utilizing multiprocessing:

We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions.

This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time/iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned.

The Quantum Annealing Genetic Algorithm is used for the mutation.

Parameters

p_size: int

The population size for the genetic algorithm. Default is 40.

p_inc_num: int

The increment number for the population size. Default is 0.

p_max: int

The maximum population size. Default is -1.

pct_random_states: int

The percentage of random states in the initial population. Default is 0.

mut_rate: int

The mutation rate for the genetic algorithm. Default is 1.

rec_rate: int

The recombination rate for the genetic algorithm. Default is 1.

rec_method: str

The recombination method for the genetic algorithm. Default is "one_point_crossover".

num_sweeps: int

The number of sweeps for the simulated annealing. Default is 1000.

num_sweeps_inc_factor: int

The increment factor for the number of sweeps. Default is 1.

num_sweeps_inc_max: int

The maximum increment for the number of sweeps. Default is -1.

beta_range_type: str

The type of beta range for the simulated annealing. Default is "default".

beta_range: str

The beta range for the simulated annealing. Default is "beta_range".

target: Any, optional

The target solution for the optimization problem.

atol: int

The absolute tolerance for the optimization problem. Default is 0.

rtol: int

The relative tolerance for the optimization problem. Default is 0.

timeout: int

The timeout for the optimization problem in seconds. Default is 5.

max_iter: int

The maximum number of iterations for the optimization problem. Default is -1.

max_consistent_iters: int

The maximum number of consistent iterations for the optimization problem. Default is -1.

return_overhead: bool

Whether to return the overhead of the optimization problem. Default is False.

use_qpu: bool

Whether to use the QPU for the optimization problem. Default is True.

Available

Parameters

{
  "p_size": 40,
  "p_inc_num": 0,
  "p_max": -1,
  "pct_random_states": 0,
  "mut_rate": 1,
  "rec_rate": 1,
  "rec_method": "one_point_crossover",
  "num_sweeps": 1000,
  "num_sweeps_inc_factor": 1,
  "num_sweeps_inc_max": -1,
  "beta_range_type": "default",
  "beta_range": "beta_range",
  "target": None,
  "atol": 0,
  "rtol": 0,
  "timeout": 5,
  "max_iter": -1,
  "max_consistent_iters": -1,
  "return_overhead": False,
  "use_qpu": True
}

QPU Provider

dwave

QAGAPW - Quantum Assisted Genetic Algorithm Pairwise

Description

QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms Pairwise:

We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions.

This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time/iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned.

The Quantum Annealing Genetic Algorithm is used for the mutation.

Parameters

p_size: int

The population size for the genetic algorithm. Default is 40.

p_inc_num: int

The increment number for the population size. Default is 0.

p_max: int

The maximum population size. Default is -1.

pct_random_states: int

The percentage of random states in the initial population. Default is 0.

mut_rate: int

The mutation rate for the genetic algorithm. Default is 1.

rec_rate: int

The recombination rate for the genetic algorithm. Default is 1.

rec_method: str

The recombination method for the genetic algorithm. Default is "one_point_crossover".

num_sweeps: int

The number of sweeps for the simulated annealing. Default is 1000.

num_sweeps_inc_factor: int

The increment factor for the number of sweeps. Default is 1.

num_sweeps_inc_max: int

The maximum increment for the number of sweeps. Default is -1.

beta_range_type: str

The type of beta range for the simulated annealing. Default is "default".

beta_range: str

The beta range for the simulated annealing. Default is "beta_range".

target: Any, optional

The target solution for the optimization problem.

atol: int

The absolute tolerance for the optimization problem. Default is 0.

rtol: int

The relative tolerance for the optimization problem. Default is 0.

timeout: int

The timeout for the optimization problem in seconds. Default is 5.

max_iter: int

The maximum number of iterations for the optimization problem. Default is -1.

max_consistent_iters: int

The maximum number of consistent iterations for the optimization problem. Default is -1.

return_overhead: bool

Whether to return the overhead of the optimization problem. Default is False.

use_qpu: bool

Whether to use the QPU for the optimization problem. Default is True.

Available

Parameters

{
  "p_size": 40,
  "p_inc_num": 0,
  "p_max": -1,
  "pct_random_states": 0,
  "mut_rate": 1,
  "rec_rate": 1,
  "rec_method": "one_point_crossover",
  "num_sweeps": 1000,
  "num_sweeps_inc_factor": 1,
  "num_sweeps_inc_max": -1,
  "beta_range_type": "default",
  "beta_range": "beta_range",
  "target": None,
  "atol": 0,
  "rtol": 0,
  "timeout": 5,
  "max_iter": -1,
  "max_consistent_iters": -1,
  "return_overhead": False,
  "use_qpu": True
}

QPU Provider

dwave

SAGA - Simulated Annealing Assisted Genetic Algorithm

Description

QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms:

We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions.

This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time/iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned.

The Simulated Annealing Genetic Algorithm is used for the mutation.

Parameters

p_size: int

The population size for the genetic algorithm. Default is 40.

p_inc_num: int

The increment number for the population size. Default is 0.

p_max: int

The maximum population size. Default is -1.

pct_random_states: int

The percentage of random states in the initial population. Default is 0.

mut_rate: int

The mutation rate for the genetic algorithm. Default is 1.

rec_rate: int

The recombination rate for the genetic algorithm. Default is 1.

rec_method: str

The recombination method for the genetic algorithm. Default is "one_point_crossover".

num_sweeps: int

The number of sweeps for the simulated annealing. Default is 1000.

num_sweeps_inc_factor: int

The increment factor for the number of sweeps. Default is 1.

num_sweeps_inc_max: int

The maximum increment for the number of sweeps. Default is -1.

beta_range_type: str

The type of beta range for the simulated annealing. Default is "default".

beta_range: str

The beta range for the simulated annealing. Default is "beta_range".

target: Any, optional

The target solution for the optimization problem.

atol: int

The absolute tolerance for the optimization problem. Default is 0.

rtol: int

The relative tolerance for the optimization problem. Default is 0.

timeout: int

The timeout for the optimization problem in seconds. Default is 5.

max_iter: int

The maximum number of iterations for the optimization problem. Default is -1.

max_consistent_iters: int

The maximum number of consistent iterations for the optimization problem. Default is -1.

return_overhead: bool

Whether to return the overhead of the optimization problem. Default is False.

Available

Parameters

{
  "p_size": 40,
  "p_inc_num": 0,
  "p_max": -1,
  "pct_random_states": 0,
  "mut_rate": 1,
  "rec_rate": 1,
  "rec_method": "one_point_crossover",
  "num_sweeps": 1000,
  "num_sweeps_inc_factor": 1,
  "num_sweeps_inc_max": -1,
  "beta_range_type": "default",
  "beta_range": "beta_range",
  "target": None,
  "atol": 0,
  "rtol": 0,
  "timeout": 5,
  "max_iter": -1,
  "max_consistent_iters": -1,
  "return_overhead": False
}

QPU Provider

SAGAMP - Simulated Annealing Assisted Genetic Algorithm Multiprocessing

Description

QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms while utilizing multiprocessing:

We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions.

This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time/iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned.

The Simulated Annealing Genetic Algorithm is used for the mutation.

Parameters

p_size: int

The population size for the genetic algorithm. Default is 40.

p_inc_num: int

The increment number for the population size. Default is 0.

p_max: int

The maximum population size. Default is -1.

pct_random_states: int

The percentage of random states in the initial population. Default is 0.

mut_rate: int

The mutation rate for the genetic algorithm. Default is 1.

rec_rate: int

The recombination rate for the genetic algorithm. Default is 1.

rec_method: str

The recombination method for the genetic algorithm. Default is "one_point_crossover".

num_sweeps: int

The number of sweeps for the simulated annealing. Default is 1000.

num_sweeps_inc_factor: int

The increment factor for the number of sweeps. Default is 1.

num_sweeps_inc_max: int

The maximum increment for the number of sweeps. Default is -1.

beta_range_type: str

The type of beta range for the simulated annealing. Default is "default".

beta_range: str

The beta range for the simulated annealing. Default is "beta_range".

target: Any, optional

The target solution for the optimization problem.

atol: int

The absolute tolerance for the optimization problem. Default is 0.

rtol: int

The relative tolerance for the optimization problem. Default is 0.

timeout: int

The timeout for the optimization problem in seconds. Default is 5.

max_iter: int

The maximum number of iterations for the optimization problem. Default is -1.

max_consistent_iters: int

The maximum number of consistent iterations for the optimization problem. Default is -1.

return_overhead: bool

Whether to return the overhead of the optimization problem. Default is False.

Available

Parameters

{
  "p_size": 40,
  "p_inc_num": 0,
  "p_max": -1,
  "pct_random_states": 0,
  "mut_rate": 1,
  "rec_rate": 1,
  "rec_method": "one_point_crossover",
  "num_sweeps": 1000,
  "num_sweeps_inc_factor": 1,
  "num_sweeps_inc_max": -1,
  "beta_range_type": "default",
  "beta_range": "beta_range",
  "target": None,
  "atol": 0,
  "rtol": 0,
  "timeout": 5,
  "max_iter": -1,
  "max_consistent_iters": -1,
  "return_overhead": False
}

QPU Provider

SAGAPW - Simulated Annealing Assisted Genetic Algorithm Pairwise

Description

QAGA hybrid solver algorithm using the paradigm of Genetic Algorithms Pairwise:

We keep track of a population of possible solutions to an optimization/decision problem in the QUBO formulation, and iteratively create new solutions from these using mutations and recombinations. A selection ensures we only keep track of the most promising solutions in the population for the next iteration, where these again are used to create new solutions.

This process is run until a predefined stopping criterion is reached, which might be a desired solution quality (i.e. an energy level) or a boundary on the time/iterations the algorithm is allowed to run. At the end, the best found solution vector and its corresponding solution value is returned.

The Simulated Annealing Genetic Algorithm is used for the mutation.

Parameters

p_size: int

The population size for the genetic algorithm. Default is 40.

p_inc_num: int

The increment number for the population size. Default is 0.

p_max: int

The maximum population size. Default is -1.

pct_random_states: int

The percentage of random states in the initial population. Default is 0.

mut_rate: int

The mutation rate for the genetic algorithm. Default is 1.

rec_rate: int

The recombination rate for the genetic algorithm. Default is 1.

rec_method: str

The recombination method for the genetic algorithm. Default is "one_point_crossover".

num_sweeps: int

The number of sweeps for the simulated annealing. Default is 1000.

num_sweeps_inc_factor: int

The increment factor for the number of sweeps. Default is 1.

num_sweeps_inc_max: int

The maximum increment for the number of sweeps. Default is -1.

beta_range_type: str

The type of beta range for the simulated annealing. Default is "default".

beta_range: str

The beta range for the simulated annealing. Default is "beta_range".

target: Any, optional

The target solution for the optimization problem.

atol: int

The absolute tolerance for the optimization problem. Default is 0.

rtol: int

The relative tolerance for the optimization problem. Default is 0.

timeout: int

The timeout for the optimization problem in seconds. Default is 5.

max_iter: int

The maximum number of iterations for the optimization problem. Default is -1.

max_consistent_iters: int

The maximum number of consistent iterations for the optimization problem. Default is -1.

return_overhead: bool

Whether to return the overhead of the optimization problem. Default is False.

Available

Parameters

{
  "p_size": 40,
  "p_inc_num": 0,
  "p_max": -1,
  "pct_random_states": 0,
  "mut_rate": 1,
  "rec_rate": 1,
  "rec_method": "one_point_crossover",
  "num_sweeps": 1000,
  "num_sweeps_inc_factor": 1,
  "num_sweeps_inc_max": -1,
  "beta_range_type": "default",
  "beta_range": "beta_range",
  "target": None,
  "atol": 0,
  "rtol": 0,
  "timeout": 5,
  "max_iter": -1,
  "max_consistent_iters": -1,
  "return_overhead": False
}

QPU Provider