.. _multi_gate:
=====================
MultipleGate Circuit
=====================
This example solves a logiccircuit problem on a DWave quantum computer. It
expands on the discussion in the :ref:`and` example about the effect of
:term:`minorembedding` on performance, demonstrating different approaches to
embedding.
.. raw:: latex
\begin{figure}
\begin{centering}
\begin{circuitikz}
\node (in1) at (0, 6.3) {$a$};
\node (in2) at (0, 5) {$b$};
\node (in3) at (0, 2.7) {$c$};
\node (in4) at (0, 1) {$d$};
\node(out1) at (10.5, 4.1) {$z$} ;
\draw
(1.75,5) node[not port] (mynot1) {1}
(2.25,3) node[or port] (myor2) {2}
(5,6) node[and port] (myand3) {3}
(5,1) node[or port] (myor4) {4}
(7,5) node[and port] (myand5) {5}
(7,1) node[not port] (mynot6) {6}
(9.25,4) node[or port] (myor7) {7}
(0.1, 6.25)  (myand3.in 1)
(0.1, 5)  (mynot1.in)
(0.1, 5)  (myor2.in 1)
(0.1, 2.7)  (myor2.in 2)
(0.1, 0.75)  (myor4.in 2)
(mynot1.out)  (myand3.in 2)
(myor2.out)  (myor4.in 1)
(myand3.out)  (myand5.in 1)
(myor4.out)  (myand5.in 2)
(myor4.out)  (mynot6.in)
(myand5.out)  (myor7.in 1)
(mynot6.out)  (myor7.in 2)
(myor7.out)  (10.4, 4.0);
\end{circuitikz}\\
\end{centering}
\caption{Logic circuit that implements $z=a \overline{b} c + $a \overline{b}
c + b \overline{d} + c \overline{d}.}
\label{fig:logicCircuit}
\end{figure}
A simple circuit is shown in Figure \ref{fig:logicCircuit}.
.. figure:: ../_images/MultiGateCircuit.png
:name: Problem_MultiGateCircuit
:alt: image
:align: center
:scale: 90 %
Circuit with 7 logic gates, 4 inputs (:math:`a, b, c, d`), and 1 output (:math:`z`).
The circuit implements function :math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})`.
Example Requirements
====================
.. include:: hybrid_solver_service.rst
:startafter: examplerequirementsstartmarker
:endbefore: examplerequirementsendmarker
For the optional graphics, you will also need the nonopensource
:doc:`probleminspector ` Ocean package,
which you may have chosen to install in the :ref:`setup_install`
section.
Formulating the Problem as a CSP
================================
Other examples (:ref:`not` and :ref:`and`) show how a Boolean gate is
represented as a *constraint satisfaction problem* (:term:`CSP`) on a quantum
computer. This example aggregates the resulting binary quadratic models (BQM)
to do the same for multiple gates that constitute a circuit.
SmallCircuit Problem

To start with, solve the circuit with 7 logic gates shown above.
The code below represents standard Boolean gates with BQMs provided by
:doc:`dimod ` BQM generators, represents the Boolean NOT
operation by inverting the coefficients of the relevant variables, and sums the
gatelevel BQMs. The resulting aggregate BQM has its lowest value for variable
assignments that satisfy all the constraints representing the circuit's Boolean gates.
>>> from dimod.generators import and_gate, or_gate
...
>>> bqm_gate2 = or_gate('b', 'c', 'out2')
>>> bqm_gate3 = and_gate('a', 'b', 'out3')
>>> bqm_gate3.flip_variable('b')
>>> bqm_gate4 = or_gate('out2', 'd', 'out4')
>>> bqm_gate5 = and_gate('out3', 'out4', 'out5')
>>> bqm_gate7 = or_gate('out5', 'out4', 'z')
>>> bqm_gate7.flip_variable('out4')
...
>>> bqm = bqm_gate2 + bqm_gate3 + bqm_gate4 + bqm_gate5 + bqm_gate7
>>> print(bqm.num_variables)
9
This circuit is small enough to solve with a bruteforce algorithm such as that
used by the :class:`~dimod.reference.samplers.ExactSolver` class. Solve and
print all feasible solutions.
>>> from dimod import ExactSolver, drop_variables
...
>>> solutions = ExactSolver().sample(bqm).lowest()
>>> out_fields = [key for key in list(next(solutions.data(['sample'])))[0].keys() if 'out' in key]
>>> print(drop_variables(solutions, out_fields))
a b c d z energy num_oc.
0 0 0 0 1 0 0.0 1
1 0 0 1 1 0 0.0 1
2 0 1 1 1 0 0.0 1
3 0 1 0 1 0 0.0 1
4 1 1 0 1 0 0.0 1
5 1 1 1 1 0 0.0 1
6 1 1 1 0 0 0.0 1
7 1 1 0 0 0 0.0 1
8 0 1 0 0 0 0.0 1
9 0 1 1 0 0 0.0 1
10 0 0 1 0 0 0.0 1
11 1 0 0 1 1 0.0 1
12 1 0 1 1 1 0.0 1
13 1 0 1 0 1 0.0 1
14 1 0 0 0 1 0.0 1
15 0 0 0 0 1 0.0 1
['BINARY', 16 rows, 16 samples, 5 variables]
Bruteforce algorithms are not effective for large problems.
LargeCircuit Problem

In the figure below, the 7gates circuit solved above is replicated with the
outputs connected through a series of XOR gates.
.. figure:: ../_images/MultiGateCircuit_3Instances.png
:name: Problem_MultiGateCircuit_3Instances
:alt: image
:align: center
:scale: 90 %
Multiple replications of the 7gates circuit (:math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})`) connected by XOR gates.
The :code:`circuit_bqm` function defined below replicates, for a specified number
of circuits, the BQM developed above and connects the outputs through a cascade
of XOR gates.
>>> from dimod import BinaryQuadraticModel, quicksum
>>> from dimod.generators import and_gate, or_gate, xor_gate
...
>>> def circuit_bqm(n: int = 3) > BinaryQuadraticModel:
... "Create a BQM for n replications of the 7gate circuit."
...
... if n < 2:
... raise ValueError("n must be at least 2")
...
... bqm_gate2 = [or_gate(f"b_{c}", f"c_{c}", f"out2_{c}") for c in range(n)]
... bqm_gate3 = [and_gate(f"a_{c}", f"b_{c}", f"out3_{c}") for c in range(n)]
... [bqm.flip_variable("b_{}".format(indx)) for indx, bqm in enumerate(bqm_gate3)]
... bqm_gate4 = [or_gate(f"out2_{c}", f"d_{c}", f"out4_{c}") for c in range(n)]
... bqm_gate5 = [and_gate(f"out3_{c}", f"out4_{c}", f"out5_{c}") for c in range(n)]
... bqm_gate7 = [or_gate(f"out5_{c}", f"out4_{c}", f"z_{c}") for c in range(n)]
... [bqm.flip_variable("out4_{}".format(indx)) for indx, bqm in enumerate(bqm_gate7)]
... bqm_z = [xor_gate("z_0", "z_1", "zz0", "aux0")] + [
... xor_gate(f"z_{c}", f"zz{c2}", f"zz{c1}", f"aux{c1}") for c in range(2, n)]
... return quicksum(bqm_gate2 + bqm_gate3 + bqm_gate4 + bqm_gate5 + bqm_gate7 + bqm_z)
Instantiate a BQM for six replications. The resulting circuit has 64 variables.
>>> bqm = circuit_bqm(6)
>>> print(bqm.num_variables)
64
MinorEmbedding and Sampling
============================
The :ref:`and` example used the :class:`~dwave.system.composites.EmbeddingComposite`
composite to :term:`minorembed` its unstructured problem onto the topology of
a quantum computer's qubits and couplers. That composite uses the
:ref:`minorminer algorithm ` to find a minor embedding
for each problem you submit.
For some applications, the submitted problems might be related or limited
in size in such a way that you can find a common minor embedding for the entire
set of problems. For instance, you might wish to submit the circuit of this
example multiple times with various configurations of fixed inputs (inputs set to
zero or one).
One approach is to precalculate a minor embedding for a clique
(a :term:`complete graph`), a "clique embedding", of a size at least equal to the
maximum number of variables (nodes) expected in your problem. Because any BQM of
:math:`n` variables maps to a subgraph of an :math:`n`node clique\ [#]_, you
can then use the clique embedding by assigning to all unused nodes and edges a
value of zero.
.. [#]
The figure below illustrates such subgraphs. Two random
:func:`~networkx.generators.random_graphs.random_regular_graph` graphs with four
and three nodes, respectively, each with two edges per node, are plotted on a
background of a 5node clique.
.. figure:: ../_images/subgraphs_clique5.png
:name: MultiGateCircuit_SubgraphsClique5
:alt: image
:align: center
:scale: 50 %
Two subgraphs of a 5node clique. The clique is plotted in yellow and the
random subgraphs in blue (four nodes) and red (three nodes).
Configure a quantum computer as a sampler using both the
:class:`~dwave.system.composites.EmbeddingComposite` composed sampler, for standard
embedding, and the :class:`~dwave.system.samplers.DWaveCliqueSampler` composed
sampler, for clique embedding.
.. include:: min_vertex.rst
:startafter: defaultconfigstartmarker
:endbefore: defaultconfigendmarker
>>> from dwave.system import DWaveSampler, EmbeddingComposite, DWaveCliqueSampler
...
>>> sampler1 = EmbeddingComposite(DWaveSampler(solver=dict(topology__type='pegasus')))
>>> sampler2 = DWaveCliqueSampler(solver=dict(name=sampler1.child.solver.name)) # doctest: +SKIP
Performance Comparison: Embedding Time

The :class:`~dwave.system.samplers.DWaveCliqueSampler` composed sampler reduces
the overhead of repeatedly finding minor embeddings for applications that submit
a sequence of problems that are all subgraphs of a clique that can be embedded
on the QPU sampling the problems.
The table below shows the minorembedding times\ [#]_ for a series of random
problems of increasing size\ [#]_. Some interesting differences are bolded.
.. note:: The times below are for a particular development environment. Embedding
times can be expected to vary for different CPUs, operating systems, etc as
well as varying across problems and executions of the heuristic algorithm.
.. listtable:: MinorEmbedding: Embedding Times
:widths: 20 10 10 30
:headerrows: 1
*  Problem Size: Nodes (Edges/Node)
 :class:`~dwave.system.composites.EmbeddingComposite` Time [sec]
 :class:`~dwave.system.samplers.DWaveCliqueSampler` Time [sec]
 Notes
*  10 (5)
 **0.06**
 **152.7**
 :class:`~dwave.system.samplers.DWaveCliqueSampler` calculates all clique
embeddings for the QPU.
*  20 (10)
 0.41
 0.11

*  30 (15)
 1.39
 0.11

*  40 (20)
 3.88
 0.13

*  50 (25)
 9.7
 0.14

*  60 (30)
 17.91
 0.16

*  70 (35)
 22.02
 0.19

*  80 (40)
 **73.74**
 **0.25**

*  90 (45)
 65.05
 0.22

*  100 (50)
 28.92
 0.25
 For this execution, :class:`~dwave.system.composites.EmbeddingComposite`
found a good embedding quickly; other executions might not.
.. [#] The times are approximate: the code measures the blocking time when
submitting problems, of which minorembedding is the major element.
You can see that while the first submission is slow for the
:class:`~dwave.system.samplers.DWaveCliqueSampler` composed sampler, subsequent
submissions are fast. For the :class:`~dwave.system.composites.EmbeddingComposite`
composed sampler, the time depends on the size and complexity of each problem,
can vary between submissions of the same problem, and each submission incurs the
cost of finding an embedding anew.
.. [#]
The code below can take several minutes to run. Uncommenting the print statement
lets you view the execution's progress. Note that if you have already
used :class:`~dwave.system.samplers.DWaveCliqueSampler`, your cache might need
to be cleared with the :func:`minorminer.busclique.busgraph_cache.clear_all_caches`
function to measure the initial calculation time.
>>> import time
>>> import networkx as nx
...
>>> samplers = {"sampler1": sampler1, "sampler2": sampler2} # doctest: +SKIP
>>> times = {key: [] for key in samplers.keys()} # doctest: +SKIP
>>> for name, sampler in samplers.items(): # doctest: +SKIP
... for i in range(10):
... nodes = 10 + 10*i
... edges = 5 + 5*i
... G = nx.random_regular_graph(n=nodes, d=edges)
... # print("Submitting problem of {} nodes and {} edges to sampler {}".format(nodes, edges, name))
... times[name].append(time.time())
... sampler.sample_ising({}, {edge: 1 for edge in G.edges})
... times[name][i] = time.time()  times[name][i]
For example, to submit a problem with two replications of the
:math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})` circuit, which
has 20 variables (and 34 interactions), you can expect an embedding time of
less than a second.
>>> bqm1 = circuit_bqm(2)
>>> bqm2 = bqm1.copy(bqm1)
>>> bqm1.fix_variables([('b_0', 0), ('b_1', 0), ('a_0', 1)])
>>> bqm2.fix_variables([('b_0', 1), ('b_1', 1), ('a_0', 0)])
...
>>> bqms = [bqm1, bqm2]
>>> times = {key: [] for key in samplers.keys()} # doctest: +SKIP
>>> for name, sampler in samplers.items(): # doctest: +SKIP
... for i, bqm in enumerate(bqms):
... times[name].append(time.time())
... sampler.sample(bqm, num_reads=5000)
... times[name][i] = time.time()  times[name][i]
>>> times # doctest: +SKIP
{'sampler1': [0.43480920791625977, 0.381976842880249],
'sampler2': [0.12912440299987793, 0.12194108963012695]}
If, however, you wish to submit a problem with ten replications repeatedly, with
some subset of the variables fixed, the overhead cost of embedding can be minutes.
Performance Comparison: Solution Quality

The :class:`~dwave.system.composites.EmbeddingComposite` composed sampler attempts
to find a good embedding for any given problem while the
:class:`~dwave.system.samplers.DWaveCliqueSampler` reuses a clique embedding
found once. Typically the former results in an embedding with shorter chains than
the latter, with the difference in length increasing for larger problems.
Chain length can significantly affect solution quality.
The table below compares, for the two embedding methods, the ratio of ground
states (solutions for which the value of the BQM is zero, meaning no constraint
is violated or all assignments of values to the problem's variables match valid
states of the circuit represented by the BQM) to total samples returned from the
quantum computer when minorembedding a sequence of problems of increasing size.
The results are for one particular code\ [#]_ execution on problems that replicate
the :math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})` circuit between
two to ten times.
.. listtable:: MinorEmbedding: GroundState Ratio Across Samplers
:widths: 10 10 10
:headerrows: 1
*  Problem Size: Circuit Replications
 :class:`~dwave.system.composites.EmbeddingComposite` GroundState Ratio [%]
 :class:`~dwave.system.samplers.DWaveCliqueSampler` GroundState Ratio [%]
*  2
 69.6
 74.4
*  3
 52.6
 25.2
*  4
 40.5
 16.8
*  5
 29.1
 3.3
*  6
 20.9
 2.9
*  7
 16.3
 0.2
*  8
 14.4
 0.0
*  9
 8.9
 0.0
*  10
 7.2
 0.3
You can see that for small problems, using a clique embedding can produce
highquality solutions, and might be advantageously used when submitting a
sequence of related problems.
.. [#]
The code below can take minutes to run. Uncommenting the print statements
lets you view the execution's progress.
>>> samplers = {"sampler1": sampler1, "sampler2": sampler2} # doctest: +SKIP
>>> samplesets = {key: [] for key in samplers.keys()} # doctest: +SKIP
>>> for num_circuits in range(2, 11):
... bqm = circuit_bqm(num_circuits)
... for name, sampler in samplers.items(): # doctest: +SKIP
... # print("Submitting problem of {} circuits to sampler {}".format(num_circuits, name))
... sampleset = sampler.sample(bqm, num_reads=5000)
... if sampleset.first.energy > 0:
... samplesets[name].append(0)
... else:
... samplesets[name].append(sum(sampleset.lowest().record.num_occurrences))
Visualizing the Minor Embedding
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Ocean's :doc:`probleminspector ` can illuminate
differences in solution quality between alternative minor embeddings. You can use
it to visualize sample sets returned from a quantum computer in your browser.
>>> import dwave.inspector
>>> bqm = circuit_bqm(2)
>>> sampleset1 = sampler1.sample(bqm, num_reads=5000)
>>> dwave.inspector.show(sampleset1) # doctest: +SKIP
The figure below, constituted of snapshots from the problem inspector for
submissions to the two samplers defined above, shows that for the BQM representing
two replications of the :math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})`
circuit, with its 20 variables and 34 edges, the clique embedding uses chains of
length two to represent nodes that the standard embedding represented with
single qubits.
.. figure:: ../_images/MultiGateCircuit_Embedding2.png
:name: Problem_MultiGateCircuit_Embedding2
:alt: image
:align: center
:scale: 50 %
Graph of two replications of the :math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})`
circuit (top centre), a standard embedding (bottomleft), and a
cliqueembedding (bottomright).
Short chains of a few qubits generally enable good quality solutions: as shown
in the table above, for a tworeplications circuit the ratio of ground states is
similar for both embedding methods.
The BQM representing ten replications of the
:math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})` circuit has
over 100 variables. Typically, minor embeddings for dense graphs require longer
chains than those of sparse graphs; for this problem the clique embedding needed
chains of up to 11 qubits versus the standard embedding's chains of 2 qubits.
Depending on the problem, such chains may degrade the solution, as is the case
here.
>>> bqm = circuit_bqm(10)
>>> for sampler in samplers.values(): # doctest: +SKIP
... sampleset = sampler.sample(bqm, num_reads=5000, return_embedding=True)
... chain_len = max(len(x) for x in sampleset.info["embedding_context"]["embedding"].values())
... print("Maximum chain length: {}".format(chain_len)) # doctest: +SKIP
... dwave.inspector.show(sampleset)
Maximum chain length: 2
Maximum chain length: 11
.. figure:: ../_images/MultiGateCircuit_Embedding10.png
:name: Problem_MultiGateCircuit_Embedding10
:alt: image
:align: center
:scale: 50 %
Graph of ten replications of the :math:`z = \overline{b} (ac + ad + \overline{c}\overline{d})`
circuit (top centre), an embedding (bottomleft), a cliqueembedding
(bottomright).
The :ref:`inspector_graph_partitioning` example demonstrates how the problem
inspector can be used to analyze your problem submissions.
Performance Comparison: Embedding Executions

Algorithmic minor embedding is heuristic: solution results can vary significantly
based on the minor embedding found.
The table below compares, for a single random :func:`~dimod.generators.ran_r`
problem of a given size, the ratio of ground states to total samples returned
from the quantum computer over several executions\ [#]_ of minor embedding.
The outlier results are bolded.
For each run, the :class:`~dwave.system.composites.EmbeddingComposite` composed
sampler finds and returns a minor embedding for the problem. The returned
embedding is then reused twice by a
:class:`~dwave.system.composites.FixedEmbeddingComposite` composed sampler.
.. listtable:: MinorEmbedding: GroundState Ratio Across Executions
:widths: 10 20 10
:headerrows: 1
*  Execution
 GroundState Ratio [%]
 Chain Length
*  1
 10.1, 10.1, **8.9**
 4
*  2
 25.8, 29.1, 28.4
 4
*  3
 24.9, 24.4, 27.9
 3
*  4
 14.0, 11.0, 13.6
 3
*  5
 38.5, 36.4, 34.5
 4
*  6
 15.4, 14.6, 14.5
 4
*  7
 43.8, 44.2, **45.1**
 3
*  8
 15.9, 17.2, 15.3
 4
*  9
 31.1, 34.1, 27.1
 3
*  10
 15.3, 11.5, 12.1
 3
You can see that each set of three submission of the problem using the same
embedding have minor variations in the groundstate ratios. However, the variation
across minorembeddings of the same problem can be significant. Consequently,
for some applications it can be beneficial to run the minorembedding multiple
times and select the best embedding.
.. [#]
The code below can take minutes to run. Uncommenting the print statements
lets you view the execution's progress.
>>> from dimod.generators import ran_r
>>> from dwave.system import DWaveSampler, EmbeddingComposite, FixedEmbeddingComposite
...
>>> problem_size = 20
>>> num_problems = 10
>>> submission_repeats = 2
>>> qpu = DWaveSampler(solver=dict(topology__type='pegasus'))
>>> sampler1 = EmbeddingComposite(qpu)
>>> bqm = ran_r(10, problem_size)
...
>>> samplesets = {}
>>> chain_len = []
>>> for runs in range(num_problems):
... samplesets[runs] = []
... print("Run {}".format(runs))
... sampleset = sampler1.sample(bqm, num_reads=5000, return_embedding=True)
... if sampleset.first.energy > 0:
... samplesets[runs].append(0)
... else:
... samplesets[runs].append(sum(sampleset.lowest().record.num_occurrences))
... embedding = sampleset.info["embedding_context"]["embedding"] # doctest: +SKIP
... chain_len.append(max(len(x) for x in embedding.values())) # doctest: +SKIP
... sampler2 = FixedEmbeddingComposite(qpu, embedding=embedding) # doctest: +SKIP
...
... for i in range(submission_repeats):
... print("\tSubmitting {}{}".format(runs, i))
... sampleset = sampler2.sample(bqm, num_reads=5000, return_embedding=True) # doctest: +SKIP
... if sampleset.first.energy > 0: # doctest: +SKIP
... samplesets[runs].append(0)
... else:
... samplesets[runs].append(sum(sampleset.lowest().record.num_occurrences))