# Map Coloring¶

This example solves a map-coloring problem to demonstrate using Ocean tools to solve a problem on a D-Wave system. It demonstrates using the D-Wave system to solve a more complex constraint satisfaction problem (CSP) than that solved in the example of Constrained Scheduling.

Constraint satisfaction problems require that all a problem’s variables be assigned values, out of a finite domain, that result in the satisfying of all constraints. The map-coloring CSP, for example, is to assign a color to each region of a map such that any two regions sharing a border have different colors.

The constraints for the map-coloring problem can be expressed as follows:

Each region is assigned one color only, of \(C\) possible colors.

The color assigned to one region cannot be assigned to adjacent regions.

## Example Requirements¶

To run the code in this example, the following is required.

The requisite information for problem submission through SAPI, as described in Configuring Access to D-Wave Solvers.

Ocean tools dwavebinarycsp and dwave-system. For graphics, you will also need NetworkX.

If you installed dwave-ocean-sdk
and ran `dwave setup`

, your installation should meet these requirements.
In D-Wave’s Leap IDE, the default workspace
meets these requirements.

## Solution Steps¶

Following the standard solution process described in Section How a D-Wave System Solves Problems, (1) formulate the problem as a binary quadratic model (BQM) by using unary encoding to represent the \(C\) colors: each region is represented by \(C\) variables, one for each possible color, which is set to value \(1\) if selected, while the remaining \(C-1\) variables are \(0\). (2) Solve the BQM with a D-Wave system as the sampler.

The full workflow is as follows:

Formulate the problem as a graph, with provinces represented as nodes and shared borders as edges, using 4 binary variables (one per color) for each province.

Create a binary constraint satisfaction problem and add all the needed constraints.

Convert to a binary quadratic model.

Sample.

Plot a valid solution.

## Four-Color Canadian Map¶

This example finds a solution to the map-coloring problem for a map of Canada using four colors (the sample code can easily be modified to change the number of colors or use different maps). Canada’s 13 provinces are denoted by postal codes:

Code |
Province |
Code |
Province |
---|---|---|---|

AB |
Alberta |
BC |
British Columbia |

MB |
Manitoba |
NB |
New Brunswick |

NL |
Newfoundland and Labrador |
NS |
Nova Scotia |

NT |
Northwest Territories |
NU |
Nunavut |

ON |
Ontario |
PE |
Prince Edward Island |

QC |
Quebec |
SK |
Saskatchewan |

YT |
Yukon |

Note

You can skip directly to the complete code for the problem here: Map Coloring: Full Code.

The example uses the D-Wave binary CSP tool to set up constraints and convert the CSP to a binary quadratic model, dwave-system to set up a D-Wave system as the sampler, and NetworkX to plot results.

```
>>> import dwavebinarycsp
>>> from dwave.system import DWaveSampler, EmbeddingComposite
>>> import networkx as nx
>>> import matplotlib.pyplot as plt
```

Start by formulating the problem as a graph of the map with provinces as nodes and shared borders between provinces as edges (e.g., “(‘AB’, ‘BC’)” is an edge representing the shared border between British Columbia and Alberta).

```
>>> # Represent the map as the nodes and edges of a graph
>>> provinces = ['AB', 'BC', 'MB', 'NB', 'NL', 'NS', 'NT', 'NU', 'ON', 'PE',
... 'QC', 'SK', 'YT']
>>> neighbors = [('AB', 'BC'), ('AB', 'NT'), ('AB', 'SK'), ('BC', 'NT'), ('BC', 'YT'),
... ('MB', 'NU'), ('MB', 'ON'), ('MB', 'SK'), ('NB', 'NS'), ('NB', 'QC'),
... ('NL', 'QC'), ('NT', 'NU'), ('NT', 'SK'), ('NT', 'YT'), ('ON', 'QC')]
```

Create a binary constraint satisfaction problem based on two types of constraints, where csp is the dwavebinarycsp CSP object:

`csp.add_constraint(one_color_configurations, variables)`

represents the constraint that each node (province) select a single color, as represented by valid configurations`one_color_configurations = {(0, 0, 0, 1), (0, 0, 1, 0), (0, 1, 0, 0), (1, 0, 0, 0)}`

`csp.add_constraint(not_both_1, variables)`

represents the constraint that two nodes (provinces) with a shared edge (border) not both select the same color.

```
>>> # Function for the constraint that two nodes with a shared edge not both select
>>> # one color
>>> def not_both_1(v, u):
... return not (v and u)
...
>>> # Valid configurations for the constraint that each node select a single color
>>> one_color_configurations = {(0, 0, 0, 1), (0, 0, 1, 0), (0, 1, 0, 0), (1, 0, 0, 0)}
>>> colors = len(one_color_configurations)
...
>>> # Create a binary constraint satisfaction problem
>>> csp = dwavebinarycsp.ConstraintSatisfactionProblem(dwavebinarycsp.BINARY)
...
>>> # Add constraint that each node (province) select a single color
>>> for province in provinces:
... variables = [province+str(i) for i in range(colors)]
... csp.add_constraint(one_color_configurations, variables)
...
>>> # Add constraint that each pair of nodes with a shared edge not both select one color
>>> for neighbor in neighbors:
... v, u = neighbor
... for i in range(colors):
... variables = [v+str(i), u+str(i)]
... csp.add_constraint(not_both_1, variables)
```

Convert the CSP into a binary quadratic model so it can be solved on the D-Wave system.

```
>>> bqm = dwavebinarycsp.stitch(csp)
```

The next code sets up a D-Wave system as the sampler and requests 1000 samples.

Note

The code below sets a sampler without specifying SAPI parameters. Configure a default solver as described in Configuring Access to D-Wave Solvers to run the code as is, or see dwave-cloud-client to access a particular solver by setting explicit parameters in your code or environment variables.

```
>>> # Sample 1000 times
>>> sampler = EmbeddingComposite(DWaveSampler())
>>> sampleset = sampler.sample(bqm, num_reads=1000)
...
>>> # Check that a good solution was found
>>> sample = sampleset.first.sample
>>> if not csp.check(sample):
... print("Failed to color map. Try sampling again.")
... else:
... print(sample)
```

Note

The next code requires Matplotlib.

Plot a valid solution.

```
# Function that plots a returned sample
def plot_map(sample):
G = nx.Graph()
G.add_nodes_from(provinces)
G.add_edges_from(neighbors)
# Translate from binary to integer color representation
color_map = {}
for province in provinces:
for i in range(colors):
if sample[province+str(i)]:
color_map[province] = i
# Plot the sample with color-coded nodes
node_colors = [color_map.get(node) for node in G.nodes()]
nx.draw_circular(G, with_labels=True, node_color=node_colors, node_size=3000, cmap=plt.cm.rainbow)
plt.show()
```

```
>>> plot_map(sample)
```

The plot shows a solution returned by the D-Wave solver. No provinces sharing a border have the same color.

Note

You can copy the complete code for the problem here: Map Coloring: Full Code.