Hitachi

Hitachi has developed the CMOS annealing machine, a non-Neumann architecture computer utilizing the structure of SRAM, a storage device, to perform optimization processing with the Ising model.

API Reference

Solvers

Models	Backend
BinaryQuadraticModel, HitachiModelList	hitachi.cmos_annealer

HitachiParameterModel

Parameters:

• solver_type: An integer value representing the type of solver.
• num_executions: An integer value representing the number of executions. It defaults to 1 if not specified.
• temperature_num_steps: An integer value representing the number of steps in the temperature. It defaults to 10 if not specified.
• temperature_step_length: An integer value representing the length of each step in the temperature. It defaults to 100 if not specified.
• temperature_initial: A float value representing the initial temperature. It defaults to 10.0 if not specified.
• temperature_target: A float value representing the target temperature. It defaults to 0.01 if not specified.
• energies: A boolean value indicating whether to include energies in the output. It defaults to True if not specified.
• spins: A boolean value indicating whether to include spins in the output. It defaults to True if not specified.
• execution_time: A boolean value indicating whether to include execution time in the output. It defaults to True if not specified.
• num_outputs: An integer value representing the number of outputs. It defaults to 0 if not specified.
• averaged_spins: A boolean value indicating whether to include averaged spins in the output. It defaults to False if not specified.
• averaged_energy: A boolean value indicating whether to include averaged energy in the output. It defaults to False if not specified.

Binary Quadratic Model

import strangeworks as sw

from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import HitachiParameterModel
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}
# 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 = HitachiParameterModel(solver_type=5)

solver = 'hitachi.cmos_annealer'

optimizer = StrangeworksOptimizer(
    model=model,
    options=options,
    solver=solver
)

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)
print(results.solution)

For more information about the Hitachi solver, see the Hitachi Annealing Cloud API Reference.

Ising Model

The model parameter is an array of integer values [x0, y0, x1, y1, p] with 5 elements representing the vertices or interactions of the Ising model. The order in which they are specified does not affect the calculation results.

x and y represent the coordinates (x-axis, y-axis) on the Ising model, respectively, and p represents the coefficient. Set the Ising model in the form x0 == x1, y0 == y1 to represent the vertex = first order term (magnetic field), and set x0, y0 and x1, y1 to be adjacent to each other to represent the interaction of two vertices.

Adjacent means up-down, left-right, and diagonal. An error occurs if the coordinate specification of two non-adjacent vertices is included.

import strangeworks as sw

from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.problem_models import HitachiModelList
from strangeworks_optimization_models.parameter_models import HitachiParameterModel

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

model = HitachiModelList(
    [
        [0, 0, 0, 1, 117],
        [0, 0, 1, 0, 104],
        [0, 0, 1, 1, 65],
        [0, 1, 1, 0, 72],
        [0, 1, 1, 1, 45],
        [1, 0, 1, 1, 40],
    ]
)

options = HitachiParameterModel(solver_type=5)

solver = 'hitachi.cmos_annealer'

optimizer = StrangeworksOptimizer(
    model=model,
    options=options,
    solver=solver
)

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)
print(results.solution)