# Using the Problem Inspector¶

This example solves a graph partitioning problem to show how D-Wave’s Problem Inspector tool can help you evaluate the minor-embedding used in your problem submissions to the quantum computer.

## 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 dwave-system and dwave-inspector.

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¶

The How a D-Wave System Solves Problems section describes the process of solving problems on the quantum computer in two steps: (1) Formulate the problem as a binary quadratic model (BQM) and (2) Solve the BQM with a D-Wave system or classical sampler. In this example, a QUBO is formulated with simple math, the problem is submitted naively to the QPU, its minor embedding examined using the problem inspector, and the submission improved.

## Formulate the Problem¶

This example uses a synthetic problem for illustrative purposes: a NetworkX generated graph, NetworkX random_geometric_graph(). The problem of interest here, which is NP-hard, is to try and find the best division of the graph’s nodes into two equal sets with a minimum number of edges between the two groups.

```
import networkx as nx
graph_nodes = 16
G = nx.random_geometric_graph(n=graph_nodes, radius=.5, dim=2)
```

### Reformulate the Problem Graph as a BQM¶

This example formulates the BQM as a QUBO using the same steps described in detail in the Graph Partitioning code example of the D-Wave Code Examples GitHub repository.

```
from collections import defaultdict
from itertools import combinations
gamma = 60
Q = defaultdict(int)
# Fill in Q matrix
for u, v in G.edges:
Q[(u,u)] += 1
Q[(v,v)] += 1
Q[(u,v)] += -2
for i in G.nodes:
Q[(i,i)] += gamma*(1-len(G.nodes))
for i, j in combinations(G.nodes, 2):
Q[(i,j)] += 2*gamma
```

Print the range of values for the generated QUBO’s elements:

```
>>> print("Maximum element is {:.2f} and minimum is {:.2f}.".format(max(Q.values()), min(Q.values()))) # doctest: +SKIP
Maximum element is 120.00 and minimum is -898.00.
```

## Solve the Problem by Sampling¶

Note

Importing the problem inspector activates for the session the capture of data such as problems sent to the QPU and returned responses, relevant details of minor-embedding, and warnings. The recommended workflow is to import it at the start of your coding session or at least before submitting your problem, as is done below.

```
import numpy as np
from dwave.system import DWaveSampler, EmbeddingComposite
# Import the problem inspector to begin data capture
import dwave.inspector
sampler = EmbeddingComposite(DWaveSampler(solver={'qpu': True}))
num_reads = 1000
sampleset = sampler.sample_qubo(Q, num_reads=num_reads)
```

Check the best returned answer:

```
>>> print("Number of nodes in one set is {}, in the other, {}. \nEnergy is {}.".format(
... sum(sampleset.first.sample.values()),
... graph_nodes - sum(sampleset.first.sample.values()),
... sampleset.first.energy)) # doctest: +SKIP
Number of nodes in one set is 8, in the other, 8.
Energy is -3813.0.
```

One simple measure of the overall quality of the returned samples is the percentage of samples based on chains with high breakage rates. Here a rate above one third is chosen as the acceptable threshold:

```
>>> print("Percentage of samples with high rates of breaks is {}.".format(
... np.count_nonzero(sampleset.record.chain_break_fraction > 0.33)/num_reads*100)) # doctest: +SKIP
Percentage of samples with high rates of breaks is 78.7.
```

### Inspect the Submission¶

Use the problem inspector on the returned samples:

```
>>> dwave.inspector.show(sampleset) # doctest: +SKIP
```

The problem inspector can also display the embedded problem, showing the qubits chains viewed on a background representation of the QPU topology:

Using the same logic described in the Graph Partitioning code example, the problem is resubmitted using a higher chain strength:

```
>>> sampleset = sampler.sample_qubo(Q, num_reads=num_reads, chain_strength=1000)
```

Check the best returned answer and percentage of samples based on chains with breakage rates of over 33 percent. Results will vary due to the probabilistic nature of the quantum computer and its integrated control errors (ICE), but in this case the shown submission had a lower minimum energy and no samples based on high rates of broken chains.

```
>>> print("Number of nodes in one set is {}, in the other, {}. \nEnergy is {}.".format(
... sum(sampleset.first.sample.values()),
... graph_nodes - sum(sampleset.first.sample.values()),
... sampleset.first.energy)) # doctest: +SKIP
Number of nodes in one set is 8, in the other, 8.
Energy is -3815.0.
...
>>> print("Percentage of samples with high rates of breaks is {}.".format(
... np.count_nonzero(sampleset.record.chain_break_fraction > 0.33)/num_reads*100)) # doctest: +SKIP
Percentage of samples with high rates of breaks is 0.0.
```

If you again use the problem inspector on the returned samples, you see the improved chains.

```
>>> dwave.inspector.show(sampleset) # doctest: +SKIP
```

Also of interest is the “spread” of solution energies. For this submission there are a few distinct clusters. The problem inspector can zoom in on the lowest:

You see that most the returned solutions of lowest energy cluster closely around an energy of approximately -3807 but that the QPU has found some even lower-energy solutions. From this one can assume that it might be possible find a better solution by increasing the number of reads. Additionally however, the complete lack of broken chains for the current returned sample set suggests that the chain strength can likely be lowered while still maintaining a low rate of broken chains. Doing so enables the problem to be represented more accurately on the QPU.

```
>>> sampleset = sampler.sample_qubo(Q, num_reads=num_reads, chain_strength=300)
```

Below is one run of a few iterations of adjusting chain strength. Notice that the acceptable rate of chain breaks was set lower, to breakage rates of over 5 percent.

```
>>> print("Number of nodes in one set is {}, in the other, {}. \nEnergy is {}.".format(
... sum(sampleset.first.sample.values()),
... graph_nodes - sum(sampleset.first.sample.values()),
... sampleset.first.energy)) # doctest: +SKIP
Number of nodes in one set is 8, in the other, 8.
Energy is -3817.0.
...
>>> print("Percentage of samples with >5 percent chain breaks is {}.".format(
... np.count_nonzero(sampleset.record.chain_break_fraction > 0.05)/num_reads*100)) # doctest: +SKIP
Percentage of samples with >5 percent chain breaks is 1.7000000000000002.
```

The result of the shown submission, with a chain strength of \(300\), still had less than 2% of its samples based on broken chains.