Using Different Machines¶

from amplify import *
from amplify.constraint import *

from amplify.client import FixstarsClient
    
client = FixstarsClient()
client.parameters.timeout = 1000  # Timeout is 1 second
# client.token = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"  # If you use Amplify in a local environment, enter the Amplify API token.

from amplify.client.ocean import DWaveSamplerClient
    
client_dwave = DWaveSamplerClient()
client_dwave.token = "Please enter your token."
    
# D-Wave 2000Q
client_dwave.solver = "DW_2000Q_VFYC_6"
client_dwave.parameters.num_reads = 100  # Execution count 100
    
# D-Wave Advantage
client_dwave.solver = "Advantage_system1.1"
client_dwave.parameters.num_reads = 100  # Execution count 100

from amplify.client.ocean import LeapHybridSamplerClient
    
client_leap_hybrid = LeapHybridSamplerClient()
client_leap_hybrid.token = "Please enter your token."
client_leap_hybrid.solver = "hybrid_binary_quadratic_model_version2"
client_leap_hybrid.parameters.time_limit = 3  # Timeout is 3 seconds

from amplify.client import FujitsuDA4SolverClient
    
client_fujitsu_da4 = FujitsuDA4SolverClient()
client_fujitsu_da4.token = "Please enter your token."
client_fujitsu_da4.parameters.time_limit_sec = 3  # Timeout is 3 seconds

from amplify.client import ToshibaClient
    
client_toshiba_sbm = ToshibaClient()
client_toshiba_sbm.url = "http://xxx.xxx.xxx.xxx"  # API URL
client_toshiba_sbm.parameters.timeout = 1  # Timeout is 1 second

from amplify.client import HitachiClient
    
client_hitachi = HitachiClient()
client_hitachi.token = "Please enter your token."
client_hitachi.parameters.temperature_num_steps = 10
client_hitachi.parameters.temperature_step_length = 100
client_hitachi.parameters.temperature_initial = 100.0
client_hitachi.parameters.temperature_target = 0.02

# コスト関数の定式化例
gen = BinarySymbolGenerator()
q = gen.array(2)
cost_func = -2 * q[0] * q[1] + q[0] - q[1] + 1
cost_func

# Example of constraint formulation
constraint = 2 * equal_to(q[0] + q[1], 1)
constraint

# Building a model
model = cost_func + constraint

# Building a solver
solver = Solver(client)
    
# Running the machine
result = solver.solve(model)

for s in result:
    print(f"q = {decode_solution(q, s.values)}")
    print(f"energy = {s.energy}")