Jij

Jij is part of the Strangeworks Syndicate and is working on developing quantum annealing devices.

Solvers

Models	Backends (see catalog for complete list)
BinaryQuadraticModel, JijModeling	jij.SA, jij.SQA
JijModeling	jij.LeapHybridCQM

The maximum calculation time before solver timeout is 600.0s.

Simulated Annealing

Binary Quadratic Model

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import JijSAParameterModel
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")

solver = "jij.SA"
options = JijSAParameterModel(num_sweeps=2000, num_reads=100)

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)

JijSAParameterModel

Name	Description	Default Value
feed_dict	Dictionary of coefficients for jm.Problem.	None
num_search	Number of times algorithm will be run.	1
multipliers	Multipliers for any constraints in jm.Problem	None
beta_min	Inverse temperature.	None
beta_max	Inverse temperature.	None
num_sweeps	The number of Monte-Carlo steps.	1000
num_reads	The number of samples. If None, 1 will be set.	1
initial_state	Initial state.	None
updater	Updater algorithm. "single spin flip" or "swendsen wang".	single spin flip
sparse	If True, only non-zero matrix elements are stored, which will save memory.	False
reinitialize_state	If True, reinitialize state for each run.	True
seed	Seed for Monte Carlo algorithm.	None
needs_square_constraints	This dictionary object is utilized to determine whether to square the constraint condition while incorporating it into the QUBO/HUBO penalty term. Here, the constraint's name is used as the key. If the value is set to True, the corresponding constraint is squared upon its addition to the QUBO/HUBO penalty term.	True for linear constraints, and to False for non-linear ones.
relax_as_penalties	This dictionary object is designed to regulate the incorporation of constraint conditions into the QUBO/HUBO penalty term, with the constraint's name functioning as the key. If the key's value is True, the respective constraint is added to the QUBO/HUBO penalty term. If the value is False, the constraint is excluded from the penalty term, though it remains subject to evaluation to verify if it meets the constraint conditions.	True

Simulated Quantum Annealing

BinaryQuadraticModel

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import JijSQAParameterModel
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")

solver = "jij.SQA"
options = JijSQAParameterModel(num_sweeps=2000, num_reads=100)

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)

JijSQAParameterModel

Name	Description	Default Value
feed_dict	Dictionary of coefficients for jm.Problem.	None
num_search	Number of times algorithm will be run.	1
multipliers	Multipliers for any constraints in jm.Problem	None
beta	Inverse temperature.	None
gamma	Strength of transverse field.	None
num_sweeps	The number of Monte-Carlo steps.	1000
num_reads	The number of samples.	1
sparse	If True, only non-zero matrix elements are stored, which will save memory.	False
reinitialize_state	If True, reinitialize state for each run.	True
seed	Seed for Monte Carlo algorithm.	None
needs_square_constraints	This dictionary object is utilized to determine whether to square the constraint condition while incorporating it into the QUBO/HUBO penalty term. Here, the constraint's name is used as the key. If the value is set to True, the corresponding constraint is squared upon its addition to the QUBO/HUBO penalty term.	True for linear constraints, and to False for non-linear ones.
relax_as_penalties	This dictionary object is designed to regulate the incorporation of constraint conditions into the QUBO/HUBO penalty term, with the constraint's name functioning as the key. If the key's value is True, the respective constraint is added to the QUBO/HUBO penalty term. If the value is False, the constraint is excluded from the penalty term, though it remains subject to evaluation to verify if it meets the constraint conditions.	True

Leap Hybrid CQM

Constrained Quadratic Model

To submit a constrained quadratic model to the LeapHybridCQM solver through Jij, we must use JijModeling to define the problem.

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import JijLeapHybridCQMParameterModel
import jijmodeling as jm

d = jm.Placeholder("d", ndim=1)  # Define variable d
d.len_at(0, latex="N")  # Set latex expression of the length of d
x = jm.BinaryVar("x", shape=(d.shape[0],))  # Define binary variable
i = jm.Element("i", belong_to=(0, d.shape[0]))  # Define dummy index in summation
model = jm.Problem("simple problem")  # Create problem instance
model += jm.sum(i, d[i] * x[i])  # Add objective function
model += jm.Constraint("one hot", jm.sum(i, x[i]) == 1)  # Add constraint condition
model  # Display the problem

solver = "jij.LeapHybridCQM"
options = JijLeapHybridCQMParameterModel(feed_dict={"d": [1.0, 0.1, -2.0, 1.0]}, time_limit=10)

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)

JijLeapHybridCQMParameterModel

Name	Description	Default Value
feed_dict	Dictionary of coefficients for jm.Problem. If None, an empty dictionary will be set.	{}
num_search	Number of times algorithm will be run. If None, 1 will be set.	1
time_limit	Time limit in seconds. If None, this will be set automatically.	None