NEC

NEC is developing quantum annealers using superconducting parametron qubits with a greater number of all-to-all connected qubits.

Solver

Models	Backend
BinaryQuadraticModel, QuboDict	nec.vector_annealer

QUBO Dictionary

The native NEC QUBO Dictionary is a representation of a Quadratic Unconstrained Binary Optimization (QUBO) problem. In the QUBO Dictionary, each key-value pair represents a term in the QUBO problem. The keys are tuples that represent the variables involved in the term, and the values represent the coefficients of those terms.

For example, a QUBO Dictionary may look like this:

QuboDict({("x1", "x1"): 1.0, ("x1", "x2"): -2.0})

In this case, the QUBO problem has two variables, x1 and x2. The first term (x1, x1) has a coefficient of 1.0, and the second term (x1, x2) has a coefficient of -2.0.

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import NECParameterModel
from strangeworks_optimization_models.problem_models import QuboDict

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

model = QuboDict({("x1", "x1"): 1.0, ("x1", "x2"): -2.0})

options = NECParameterModel(offset=0, num_reads=2, num_results=2, timeout=100)

solver = "nec.vector_annealer"

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)

Binary Quadratic Model

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import NECParameterModel
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 = NECParameterModel(offset=0, num_reads=2, num_results=2, timeout=100)

solver = "nec.vector_annealer"

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)

NECParameterModel

You can set the parameters for the NEC backend using the NECParameterModel class. The (optional) parameters are:

Name	Type	Description	Default	Values (Range)
offset	float	Adjustment applied to normalize weights in the QUBO formulation.	0.0	[-inf, inf]
num_reads	int	Number of annealing sweeps to perform, determining solution attempts.	1	[1, 20]
num_results	int	Specifies the number of top solutions to retrieve from the annealing process.	1	[1, num_reads]
timeout	int	Time limit in seconds for the job execution, impacting the duration of VA.	1800	[1, 7200]
num_sweeps	int	Number of sweeps to perform per annealing.	500	[1, 100000]
beta_range	List[float, float, int]	VA beta value range in the format [start, end, steps].	[10.0, 100.0, 200]	start: [1.1754945E-38, 3.402823E+38], end: [1.1754945E-38, 3.402823E+38], steps: [1, 100000]
dense	bool	VA matrix mode. None: automatic selection, True: dense matrix mode, False: operates in sparse matrix mode.	None	[None, True, False]
vector_mode	str	Mode during VA annealing. Single speed/accuracy.	speed	[speed, accuracy]
Ve_num	int	Number of Vector Engines (VEs) engaged in the annealing process.	1	[1, max VE available]
onehot	int	Parameter for enforcing a one-hot encoding constraint during annealing.	0	[0, 1]
fixed	int	Applies a fixed value constraint to specified variables during annealing.	0	[0, 1]
andzero	int	Ensures specified variable combinations result in zero in the solution.	0	[0, 1]
orone	int	Ensures at least one variable in a specified set equals one in the solution.	0	[0, 1]
supplement	int	Additional constraints parameter to aid in exploring the solution space.	0	[0, 1]
maxone	int	Limits the maximum number of ones that can appear in a solution subset.	0	[0, inf]
minmaxone	int	Sets a minimum limit on the maximum number of ones allowed in any solution subset.	0	[0, inf]
init_spin	int	Initial setting of spins, defining the starting state for the annealing process.	0	[-1, 1]
spin_list	List[int]	List of initial spin values used to define constraints or initial conditions.	None	Specific to setup