.. _map_dqm:

================================
Map Coloring: Hybrid DQM Sampler
================================

This example solves the same map coloring problem of :ref:`map_kerberos` to
demonstrate `Leap <https://cloud.dwavesys.com/leap/>`_\ 's hybrid discrete
quadratic model (:term:`DQM`) solver, which enables you to solve problems
of arbitrary structure and size for variables with **discrete** values.

See :ref:`map_kerberos` for a description of the map coloring
:doc:`constraint satisfaction problem </concepts/csp>` (CSP).

The :ref:`map_coloring` advanced example demonstrates lower-level coding of a similar
problem, which gives the user more control over the solution procedure but requires
the knowledge of some system parameters (e.g., knowing the maximum number of supported
variables for the problem). Example :ref:`hybrid1` demonstrates the hybrid approach to
problem solving in more detail by explicitly configuring the classical and quantum workflows.

Example Requirements
====================

.. include:: hybrid_solver_service.rst
  :start-after: example-requirements-start-marker
  :end-before: example-requirements-end-marker

.. note:: This example requires a minimal understanding of the :term:`penalty model`
   approach to solving problems by minimizing penalties. In short, you formulate
   the problem using positive values to penalize undesirable outcomes; that is,
   the problem is formulated as an :term:`objective function` where desirable
   solutions are those with the lowest values. This is demonstrated in
   `Leap <https://cloud.dwavesys.com/leap/>`_\ 's *Structural Imbalance* demo,
   introduced in the :ref:`and` example, and comprehensively explained in the
   :std:doc:`Problem Solving Handbook <sysdocs_gettingstarted:doc_handbook>`_.

Solution Steps
==============

.. include:: hybrid_solver_service.rst
  :start-after: example-steps-start-marker
  :end-before: example-steps-end-marker

In this example, a DQM is created to formulate the problem and submitted to the
`Leap <https://cloud.dwavesys.com/leap/>`_ hybrid DQM solver,
``hybrid_binary_quadratic_model_version<x>``.

Formulate the Problem
=====================

This example uses the `NetworkX <https://networkx.org>`_
:func:`~networkx.readwrite.adjlist.read_adjlist` function to read a text file,
``usa.adj``, containing the states of the USA and their adjacencies (states with
a shared border) into a graph. The original map information was found on
`write-only blog of Gregg Lind <https://writeonly.wordpress.com/2009/03/20/adjacency-list-of-states-of-the-united-states-us/>`_ and looks like this::

    # Author Gregg Lind
    # License:  Public Domain.    I would love to hear about any projects you use if it for though!
    #
    AK,HI
    AL,MS,TN,GA,FL
    AR,MO,TN,MS,LA,TX,OK
    AZ,CA,NV,UT,CO,NM
    CA,OR,NV,AZ
    CO,WY,NE,KS,OK,NM,AZ,UT

    # Snipped here for brevity

You can see in the first non-comment line that the state of Alaska ("AK") has Hawaii
("HI") as an adjacency and that Alabama ("AL") shares borders with four states.

>>> import networkx as nx
>>> G = nx.read_adjlist('usa.adj', delimiter = ',')   # doctest: +SKIP

Graph G now represents states as vertices and each state's neighbors as shared edges.

>>> states = G.nodes        # doctest: +SKIP
>>> borders = G.edges       # doctest: +SKIP

You can now create a :class:`dimod.DiscreteQuadraticModel` object to represent
the problem. Because any planar map can be colored with four colors or fewer,
represent each state with a discrete variable that has four *cases* (binary
variables can have two values; discrete variables can have some arbitrary
number of cases).

For every pair of states that share a border, set a quadratic bias of :math:`1`
between the variables' identical cases and :math:`0` between all different cases
(by default, the quadratic bias is zero). Such as :term:`penalty model` adds
a value of :math:`1` to solutions of the DQM for every pair of neighboring states
with the same color. Optimal solutions are those with the fewest such neighboring
states.

>>> import dimod
...
>>> colors = [0, 1, 2, 3]
...
>>> dqm = dimod.DiscreteQuadraticModel()
>>> for state in states:            # doctest: +SKIP
...    dqm.add_variable(4, label=state)
>>> for state0, state1 in borders:          # doctest: +SKIP
...    dqm.set_quadratic(state0, state1, {(color, color): 1 for color in colors})

Solve the Problem by Sampling
=============================

D-Wave's quantum cloud service provides cloud-based hybrid solvers you can submit
arbitrary BQMs and DQMs to. These solvers, which implement state-of-the-art
classical algorithms together with intelligent allocation of the quantum
processing unit (QPU) to parts of the problem where it benefits most, are designed
to accommodate even very large problems. Leap's solvers can relieve you of the
burden of any current and future development and optimization of hybrid
algorithms that best solve your problem.

Ocean software's :doc:`dwave-system </docs_system/sdk_index>`
:class:`~dwave.system.samplers.LeapHybridDQMSampler` class enables you to easily
incorporate Leap's hybrid DQM solvers into your application.
The solution printed below is truncated.

>>> from dwave.system import LeapHybridDQMSampler
...
>>> sampleset = LeapHybridDQMSampler().sample_dqm(dqm,
...                 label='SDK Examples - Map Coloring DQM')  # doctest: +SKIP
...
>>> print("Energy: {}\nSolution: {}".format(
...        sampleset.first.energy, sampleset.first.sample))  # doctest: +SKIP
Energy: 0.0
Solution: {'AK': 0, 'AL': 0, 'MS': 3, 'TN': 1, 'GA': 2, 'FL': 3, 'AR': 2,
# Snipped here for brevity

The energy value of zero above signifies that this first (best) solution found
has accumulated no penalties, meaning no pairs of neighboring states with the
same color.

.. note:: The next code requires `Matplotlib <https://matplotlib.org>`_\ .

Plot the best solution.

>>> import matplotlib.pyplot as plt       # doctest: +SKIP
>>> node_list = [list(G.nodes)[x:x+10] for x in range(0, 50, 10)]   # doctest: +SKIP
>>> node_list[4].append('ND')     # doctest: +SKIP
# Adjust the next line if using a different map
>>> nx.draw(G, pos=nx.shell_layout(G, nlist = node_list), with_labels=True,
...         node_color=list(sampleset.first.sample.values()), node_size=400,
...         cmap=plt.cm.rainbow)                 # doctest: +SKIP
>>> plt.show()    # doctest: +SKIP

The graphic below shows the result of one such run.

.. figure:: ../_images/usa_map_dqm.png
   :name: USA_MapColoring
   :alt: image
   :align: center
   :scale: 70 %

   One solution ``hybrid_binary_quadratic_model_version1`` found for the USA map-coloring problem.