.. _hybrid1:
===========================
Problem With Many Variables
===========================
This example solves a graph problem with too many variables to fit onto the QPU.
.. figure:: ../_images/hybrid_example1.png
:name: HybridBarabasiAlbertGraph
:alt: image
:align: center
:scale: 70 %
Problem Graph with Many Variables.
The purpose of this example is to illustrate a hybrid solution---the combining of
classical and quantum resources---to a problem that cannot be mapped in its entirety
to the D-Wave system due to the number of its variables. Hard optimization problems
might have many variables; for example, scheduling or allocation of resources. In such cases,
quantum resources are used as an accelerator much as GPUs are for graphics.
.. note:: For fully connected graphs, the number of edges grows very quickly with
increased nodes, degrading performance. The current example uses 100 nodes
but with a degree of three (each node connects to three other nodes). You can
increase the number of nodes substantially as long as you keep the graph sparse.
For more detailed examples of using :doc:`dwave-hybrid `
to combine classical and quantum resources in solving your problem, see the
`Hybrid Computing Jupyter Notebooks `_.
Example Requirements
====================
.. include:: hybrid_solver_service.rst
:start-after: example-requirements-start-marker
:end-before: example-requirements-end-marker
Solution Steps
==============
.. include:: hybrid_solver_service.rst
:start-after: example-steps-start-marker
:end-before: example-steps-end-marker
This example uses :doc:`dwave-hybrid ` to combine a tabu
search on a CPU with the submission of parts of the (large) problem to a D-Wave
quantum computer.
Formulate the Problem
=====================
This example uses a synthetic problem for illustrative purposes: a NetworkX
generated graph,
`NetworkX barabasi_albert_graph() `_
, with random +1 or -1 couplings assigned to its edges.
.. testcode::
# Represent the graph problem as a binary quadratic model
import dimod
import networkx as nx
import random
graph = nx.barabasi_albert_graph(100, 3, seed=1) # Build a quasi-random graph
# Set node and edge values for the problem
h = {v: 0.0 for v in graph.nodes}
J = {edge: random.choice([-1, 1]) for edge in graph.edges}
bqm = dimod.BQM(h, J, vartype=dimod.SPIN)
Create a Hybrid Workflow
========================
The following simple workflow uses a :code:`RacingBranches` class to iterate two
:code:`Branch` classes in parallel: a tabu search, :code:`InterruptableTabuSampler`,
which is interrupted to potentially incorporate samples from subproblems (subsets of the problem
variables and structure) by :code:`EnergyImpactDecomposer | QPUSubproblemAutoEmbeddingSampler | SplatComposer`, which decomposes the
problem by selecting variables with the greatest energy impact, submits these to
the D-Wave system, and merges the subproblem's samples into the latest problem samples.
In this case, subproblems contain 30 variables in a rolling window that can cover up
to 75 percent of the problem's variables.
.. testcode::
# Set a workflow of tabu search in parallel to submissions to a D-Wave system
import hybrid
workflow = hybrid.Loop(
hybrid.RacingBranches(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(size=30, rolling=True, rolling_history=0.75)
| hybrid.QPUSubproblemAutoEmbeddingSampler()
| hybrid.SplatComposer()) | hybrid.ArgMin(), convergence=3)
Solve the Problem Using Hybrid Resources
========================================
Once you have a hybrid workflow, you can run and tune it within the dwave-hybrid framework
or convert it to a `dimod` sampler.
.. testcode::
# Convert to dimod sampler and run workflow
result = hybrid.HybridSampler(workflow).sample(bqm)
While the tabu search runs locally, one or more subproblems are sent to the QPU.
>>> print("Solution: sample={}".format(result.first)) # doctest: +SKIP
Solution: sample=Sample(sample={0: -1, 1: -1, 2: -1, 3: 1, 4: -1, ... energy=-169.0, num_occurrences=1)