Introduction

dwave-inspector provides a graphic interface for examining D-Wave quantum computers’ problems and answers. As described in the Ocean documentation’s Getting Started, the D-Wave system solves problems formulated as binary quadratic models (BQM) that are mapped to its qubits in a process called minor-embedding. Because the way you choose to minor-embed a problem (the mapping and related parameters) affects solution quality, it can be helpful to see it.

For example, minor-embedding a problem represented by a \(K_5\) fully-connected graph into an Advantage QPU, with its Pegasus topology, requires representing one of the five variables with a chain of two physical qubits:

Five-variable \(K_5\) fully-connected problem, shown on the left as a graph, is embedded in six qubits on an Advantage, shown on the right against the Pegasus topology. Variable 0, highlighted in dark magenta, is represented by two qubits, 1975 and 4840 in this particular embedding.

The problem inspector shows you your chains at a glance: you see lengths, any breakages, and physical layout.

Usage and Examples

Import the problem inspector to enable it1 to hook into your problem submissions.

1

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 as is typical for Python packages (it is also possible, but less convenient, to specify in the submission that data such as embedding be returned with the response).

The following examples demonstrate the use of the show() method to visualize an embedded problem and a logical problem in your default browser.

Inspecting an Embedded Problem

This example shows the canonical usage: samples representing physical qubits on a quantum processing unit (QPU).

>>> from dwave.system import DWaveSampler
>>> import dwave.inspector
...
>>> # Get solver
>>> sampler = DWaveSampler(solver=dict(topology__type='pegasus'))
...
>>> # Define a problem (actual qubits depend on the selected QPU's working graph)
>>> h = {}
>>> J = {(2136, 4181): -1, (2136, 2151): -0.5, (2151, 4196): 0.5, (4181, 4196): 1}
>>> all(edge in sampler.edgelist for edge in J)     
True
>>> # Sample
>>> response = sampler.sample_ising(h, J, num_reads=100)    
...
>>> # Inspect
>>> dwave.inspector.show(response)   

Edge values between qubits 2136, 4181, 2151, and 4196, and the selected solution, are shown by color on the left; a histogram, on the right, shows the energies of returned samples.

Inspecting a Logical Problem

This example visualizes a problem specified logically and then automatically minor-embedded by Ocean’s EmbeddingComposite. For illustrative purposes it sets a weak2 chain_strength to show broken chains.

Define a problem and sample it for solutions:

>>> from dwave.system import DWaveSampler, EmbeddingComposite
>>> import dimod
>>> import dwave.inspector
...
>>> # Define problem
>>> bqm = dimod.generators.doped(1, 5)
>>> bqm.add_linear_from({v: 1 for v in bqm.variables})
...
>>> # Get sampler
>>> sampler = EmbeddingComposite(DWaveSampler())
...
>>> # Sample with low chain strength
>>> sampleset = sampler.sample(bqm, num_reads=1000, chain_strength=1)
...
>>> # Inspect the problem::
>>> dwave.inspector.show(sampleset)     

The logical problem, on the left, shows that the value for variable 4 is based on a broken chain; the embedded problem, on the right, highlights the broken chain (its two qubits have different values) in bold red.

2

The default chain strength was about 3 for similar problems.