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NEC

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

Solver​

ModelsBackend
BinaryQuadraticModel, QuboDictnec.vector_annealer

NECParameterModel​

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

  • offset (float): The offset for the normalized weight information stored in the qubo.
  • num_reads (int): The number of VA annealing sweeps.
  • num_results (int): The number of VA annealing results.
  • timeout (int): The job execution timeout.
  • Ve_num (int): The number of VEs used in VA annealing.
  • onehot (int): VA onehot constraint parameter.
  • fixed (int): VA fixed constraint parameter.
  • andzero (int): VA andzero constraint parameter.
  • orone (int): VA orone constraint parameter.
  • supplement (int): VA supplement constraint parameter.
  • maxone (int): VA maxone constraint parameter.
  • minmaxone (int): VA minmaxone constraint parameter.
  • init_spin (int): VA initial spin parameter.
  • spin_list (List[int]): VA spin list parameter.

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"

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

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

results = optimizer.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"

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

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

results = optimizer.results(sw_job.slug)