def solve_qsage_example():
# use a local solver
solver = local_connection.get_solver("c4-sw_sample")
num_vars = 12
# the first parameter is an objective function, the second parameter is
# the number of variables, the third parameter is the solver, the fourth
# parameter is the parameters for solver, the fifth parameter is the
# parameters for qsage
answer = solve_qsage(ObjClass(), num_vars, solver, {}, {"verbose": 2})
print "solve_qsage ObjClass() answer: ", answer
answer = solve_qsage(obj_function, num_vars, solver, {}, {"verbose": 2})
print "solve_qsage obj_function answer: ", answer
def embedding_example():
# formulate k_6 structured graph
h = [1, 1, 1, 1, 1, 1]
J = {(i, j): 1 for i in range(6) for j in range(i)}
solver = local_connection.get_solver("c4-sw_optimize")
A = get_hardware_adjacency(solver)
# find and print embeddings for problem graph
embeddings = find_embedding(J, A, verbose=1)
print "embeddings are: ", embeddings
# embed the problem into solver graph
(h0, j0, jc, new_emb) = embed_problem(h, J, embeddings, A)
print "embedded problem result:\nj0: ", j0
print "jc: ", jc
# find unembedded results for chain strengths -0.5, -1.0, -2.0
for chain_strength in (-0.5, -1.0, -2.0):
# set chain strength values
jc = dict.fromkeys(jc, chain_strength)
# create new J array concatenating j0 with jc
emb_j = j0.copy()
emb_j.update(jc)
# solve embedded problem
answer = solve_ising(solver, h0, emb_j, num_reads=10)
# unembed and print result of the form:
# solution [solution #]
# var [var #] : [var value] ([qubit index] : [original qubit value] ...)
result = unembed_answer(answer['solutions'],
new_emb,
broken_chains="minimize_energy",
h=h,
j=J)
print "result for chain strength = ", chain_strength
for i, (embsol, sol) in enumerate(zip(answer['solutions'], result)):
print "solution", i
for j, emb in enumerate(embeddings):
print "var %d: %d (" % (j, sol[j]),
for k in emb:
print "%d:%d" % (k, embsol[k]),
print ")"
def embedding_example():
# formulate k_6 structured graph
h = [1, 1, 1, 1, 1, 1]
J = {(i, j): 1 for i in range(6) for j in range(i)}
solver = local_connection.get_solver("c4-sw_optimize")
A = get_hardware_adjacency(solver)
# find and print embeddings for problem graph
embeddings = find_embedding(J, A, verbose=1)
print "embeddings are: ", embeddings
# embed the problem into solver graph
(h0, j0, jc, new_emb) = embed_problem(h, J, embeddings, A)
print "embedded problem result:\nj0: ", j0
print "jc: ", jc
# find unembedded results for chain strengths -0.5, -1.0, -2.0
for chain_strength in (-0.5, -1.0, -2.0):
# set chain strength values
jc = dict.fromkeys(jc, chain_strength)
# create new J array concatenating j0 with jc
emb_j = j0.copy()
emb_j.update(jc)
# solve embedded problem
answer = solve_ising(solver, h0, emb_j, num_reads=10)
# unembed and print result of the form:
# solution [solution #]
# var [var #] : [var value] ([qubit index] : [original qubit value] ...)
result = unembed_answer(answer['solutions'], new_emb, broken_chains="minimize_energy", h=h, j=J)
print "result for chain strength = ", chain_strength
for i, (embsol, sol) in enumerate(zip(answer['solutions'], result)):
print "solution", i
for j, emb in enumerate(embeddings):
print "var %d: %d (" % (j, sol[j]),
for k in emb:
print "%d:%d" % (k, embsol[k]),
print ")"
from dwave_sapi2.util import get_hardware_adjacency
from dwave_sapi2.embedding import embed_problem, unembed_answer
from dwave_sapi2.core import solve_ising
from dwave_sapi2.local import local_connection
#~ Connecting to the solver
#~ ^^^^^^^^^^^^^^^^^^^^^^^^
#~
#~ Here, we're going to use a local solver because various users will have
#~ access to different systems. When you extend this code, you'll want to
#~ modify it to use a remote connection with your API key, and connect to
#~ the appropriate solver.
#~
solver_name = "c4-sw_sample"
solver = local_connection.get_solver(solver_name)
# or, if you're using a remote solver, uncomment this block and put your
# authentication information here
# from dwave_sapi2.core import remote
# sapi_url = 'https://cloud.dwavesys.com/sapi'
# sapi_token = 'ENTER SAPI TOKEN'
# solver_name = 'DW_2000Q_5'
# connection = remote.RemoteConnection(sapi_url, sapi_token)
# solver = connection.get_solver(solver_name)
#~
#~ Now that we're connected, let's pull down the adjacency information from
#~ the solver. This is just a set of tuples ``(p, q)`` of qubit labels. Using
#~ the ``c4-sw_optimize`` solver, this is simply a :math:`C_{4,4,4}` Chimera
#~ graph. If you're using a hardware solver, this will be something more
def __init__(self, solver_name):
dimod.TemplateSampler.__init__(self)
self.solver = solver = local_connection.get_solver(solver_name)
edges = get_hardware_adjacency(solver)
self.structure = (set().union(*edges), edges)
from dwave_sapi2.embedding import embed_problem, unembed_answer
from dwave_sapi2.core import solve_ising
from dwave_sapi2.local import local_connection
from random import uniform, randint
#~ Connecting to the solver
#~ ^^^^^^^^^^^^^^^^^^^^^^^^
#~
#~ Here, we're going to use a local solver because various users will have
#~ access to different systems. When you extend this code, you'll want to
#~ modify it to use a remote connection with your API key, and connect to
#~ the appropriate solver.
#~
solver_name = "c4-sw_sample"
solver = local_connection.get_solver(solver_name)
# or, if you're using a remote solver, uncomment this block and put your
# authentication information here
# from dwave_sapi2.core import remote
# sapi_url = 'https://dw2x.dwavesys.com/sapi'
# sapi_token = 'ENTER SAPI TOKEN'
# solver_name = 'DW2X'
# connection = remote.RemoteConnection(sapi_url, sapi_token)
# solver = connection.get_solver(solver_name)
#~
#~ Now that we're connected, let's pull down the adjacency information from
#~ the solver. This is just a set of tuples ``(p, q)`` of qubit labels. Using
#~ the ``c4-sw_optimize`` solver, this is simply a :math:`C_{4,4,4}` Chimera
#~ graph. If you're using a hardware solver, this will be something more
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# https://docs.dwavesys.com/docs/latest/c_timing_6.html
from dwave_sapi2.local import local_connection
from dwave_sapi2.core import async_solve_ising, await_completion
import datetime
h, J = build_hamiltonian()
solver = local_connection.get_solver('example_solver')
submitted_qmi = async_solve_ising(solver, h, J, num_reads=100)
await_completion([submitted_qmi], min_done=2, timeout=1.0)
# QPU and PP times
result = submitted_qmi.result()
timing = result['timing']
# Service time
time_format = "%Y-%m-%dT%H:%M:%S.%fZ"
status = submitted_qmi.status()
start_time = datetime.datetime.strptime(status['time_received'], time_format)
finish_time = datetime.datetime.strptime(status['time_solved'], time_format)
service_time = finish_time - start_time
def connect_to_local():
solver = local_connection.get_solver(sys.argv[1])
return solver
