Fujitsu

The Fujitsu Digital Annealer provides an alternative to quantum computing technology

Solver

Models	Backend
BinaryQuadraticModel, FujitsuModelList	fujitsu.DA3

Binary Quadratic Model

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import (
    FujitsuParameterModel
)
from dimod import BinaryQuadraticModel

sw.authenticate('your-api-key')

linear = {1: -2, 2: -2, 3: -3, 4: -3, 5: -2}
quadratic = {(1, 2): 2, (1, 3): 2, (2, 4): 2, (3, 4): 2, (3, 5): 2, (4, 5): 2}
model = BinaryQuadraticModel(linear, quadratic, "BINARY")

options = FujitsuParameterModel(
        time_limit_sec=1, penalty_coef=10000
    )

solver = "fujitsu.DA3"

so = StrangeworksOptimizer(model=model, solver=solver, options=options)
sw_job = so.run()

print(f"Job slug: {sw_job.slug}")
print(f"Job status: {so.status(sw_job.slug)}")

results = so.results(sw_job.slug)

BQM to FujitsuModelList

For larger models it is advisable to first convert a BinaryQuadraticModel to a FujitsuModelList before submitting to the solver since it can be more efficient

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.problem_models import FujitsuModelList
from strangeworks_optimization_models.converter_models import StrangeworksConverterFactory

sw.authenticate('your-api-key')

linear = {1: -2, 2: -2, 3: -3, 4: -3, 5: -2}
quadratic = {(1, 2): 2, (1, 3): 2, (2, 4): 2, (3, 4): 2, (3, 5): 2, (4, 5): 2}
model = BinaryQuadraticModel(linear, quadratic, "BINARY", offset=1.4)
strangeworks_converter = StrangeworksConverterFactory.from_model(model, FujitsuModelList)

fujitsu_model = strangeworks_converter.convert()

optimizer = StrangeworksOptimizer(model=fujitsu_model, solver="fujitsu.DA3")
optimizer.run()

Fujitsu Native Dictionary

For more information see: https://portal.aispf.global.fujitsu.com/apidoc/da/jp/api-ref/da-qubo-v3c-en.html.

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import (
    FujitsuParameterModel
)
from strangeworks_optimization_models.problem_models import FujitsuModelList

sw.authenticate('your-api-key')

model = FujitsuModelList(
    binary_polynomial={
        "terms": [
            {"coefficient": 2, "polynomials": [1, 2]},
            {"coefficient": -4, "polynomials": [2, 4]},
            {"coefficient": -3, "polynomials": []},
        ]
    }
)

options = FujitsuParameterModel(
        time_limit_sec=1, penalty_coef=10000
    )

solver = "fujitsu.DA3"

so = StrangeworksOptimizer(model=model,
                            solver=solver,
                            options=options)
sw_job = so.run()

print(f"Job slug: {sw_job.slug}")
print(f"Job status: {so.status(sw_job.slug)}")

results = so.results(sw_job.slug)

FujitsuParameterModel

Parameter	Description	Type	Default	Range
time_limit_sec	Maximum running time of DA in seconds	int	10	1 to 3600
target_energy	Threshold energy for fast exit	float	disabled	-
num_run	The number of parallel attempts of each group	int	1	1 to 16
num_output_solution	The number of output solutions of each group	int	5	1 to 1024
gs_level	Level of the global search	int	5	0 to 100
gs_cutoff	Global search cutoff level	int	8000	0 to 1000000
one_hot_level	Level of the 1hot constraint search	int	3	0 to 100
one_hot_cutoff	Level of the convergence for 1hot constraint search	int	100	0 to 1000000
internal_penalty	Mode of 1hot constraint internal generation	int	0	0 or 1
penalty_auto_mode	Coefficient adjustment mode	int	1	0 to 10000
penalty_coef	Coefficient of the constraint term	int	1	1 to 9223372036854775807
penalty_inc_rate	Parameters for automatic adjustment of constraint terms	int	150	100 to 200
max_penalty_coef	Maximum constraint term coefficient	int	0	0 to 9223372036854775807
guidance_config	Initial value of each variable	dict	-	-
fixed_config	Fixed value of each variable	dict	-	-
one_way_one_hot_groups	Number of variables in each group of one-way one-hot constraints	dict	-	-
two_way_one_hot_groups	Number of variables in each group of two-way one-hot constraints	dict	-	-