TOSHIBA

SQBM+ v2.0.0

SQBM+ v2.0.0 is a TOSHIBA-developed quantum-inspired optimization solution with a simulated bifurcation machine at its core.

Tip

Available as a paid option on AWS Marketplace or Fixstars Amplify.

Solver specification:

Client class	amplify.ToshibaSQBM2Client
Execution parameters	amplify.ToshibaSQBM2Client.Parameters
Execution result	amplify.ToshibaSQBM2Client.Result
Execution time	amplify.ToshibaSQBM2Client.Result.time
API method	REST API (HDF5/JSON)
API reference	📖 User Guide

• When Qubo is set in the solver property (default):

  	Binary variable	Ising variable	Integer variable	Real variable
  Objective function	2nd	-	-	-
  Equality constraint	-	-	-	-
  Inequality constraint	-	-	-	-

• When Qplib is set in the solver property (default):

  	Binary variable	Ising variable	Integer variable	Real variable
  Objective function	2nd	-	-	-
  Equality constraint	1st	-	-	-
  Inequality constraint	1st	-	-	-

Client class:

In addition to the common interface, the client class has the following attributes and a method.

Attribute	Data type	Details
solver	Solver | str	Specify the solver to use from Qubo (default) and Qplib.
compression	bool	Compress the request data before sending. The default value is True; setting to False will reduce the compression time of the request data, but may increase the communication time.

Method	Return type	Details
health_check()	bool	Query the health check API to verify that the solver is working properly.

Configuration example:

from amplify import ToshibaSQBM2Client
from datetime import timedelta

client = ToshibaSQBM2Client()

# Set API token
# (Not required if using AWS version)
client.token = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Set URL (required if using AWS version)
# client.url = "http://xxx.xxx.xxx.xxx:8000/"

# Specify a solver
# (Qubo or Qplib)
client.solver = "Qplib"

# Set execution time to 1 second
client.parameters.timeout = timedelta(milliseconds=1000)

Execution example:

from amplify import Model, VariableGenerator, solve

# Create decision variables and the objective function
g = VariableGenerator()
q = g.array("Binary", 2)
f = q[0] * q[1] + q[0] - q[1] + 1

# Create a model
model = Model(f)

# Run the solver
result = solve(model, client)

To perform health check API:

>>> client.health_check()
True

To obtain the solver version:

>>> client.version
'2.0.0'