Composites¶
dimod composites that provide layers of pre and postprocessing (e.g., minorembedding) when using the DWave system:
Other Ocean packages provide additional composites; for example, dimod provides composites that operate on the problem (e.g., scaling values), track inputs and outputs for debugging, and other useful functionality relevant to generic samplers.
CutOffs¶
Prunes the binary quadratic model (BQM) submitted to the child sampler by retaining only interactions with values commensurate with the sampler’s precision.
CutOffComposite¶
 class CutOffComposite(child_sampler, cutoff, cutoff_vartype=Vartype.SPIN, comparison=<builtin function lt>)[source]¶
Composite to remove interactions below a specified cutoff value.
Prunes the binary quadratic model (BQM) submitted to the child sampler by retaining only interactions with values commensurate with the sampler’s precision as specified by the
cutoff
argument. Also removes variables isolated post or preremoval of these interactions from the BQM passed on to the child sampler, setting these variables to values that minimize the original BQM’s energy for the returned samples. Parameters
sampler (
dimod.Sampler
) – A dimod sampler.cutoff (number) – Lower bound for absolute value of interactions. Interactions with absolute values lower than
cutoff
are removed. Isolated variables are also not passed on to the child sampler.cutoff_vartype (
Vartype
/str/set, default=’SPIN’) –Variable space to execute the removal in. Accepted input values:
Vartype.SPIN
,'SPIN'
,{1, 1}
Vartype.BINARY
,'BINARY'
,{0, 1}
comparison (function, optional) – A comparison operator for comparing interaction values to the cutoff value. Defaults to
operator.lt()
.
Examples
This example removes one interaction,
'ac': 0.7
, before embedding on a DWave system. Note that the lowestenergy sample for the embedded problem is{'a': 1, 'b': 1, 'c': 1}
but with a large enough number of samples (herenum_reads=1000
), the lowestenergy solution to the complete BQM is likely found and its energy recalculated by the composite.>>> import dimod >>> sampler = DWaveSampler(solver={'qpu': True}) >>> bqm = dimod.BinaryQuadraticModel({'a': 1, 'b': 1, 'c': 1}, ... {'ab': 0.8, 'ac': 0.7, 'bc': 1}, ... 0, ... dimod.SPIN) >>> CutOffComposite(AutoEmbeddingComposite(sampler), 0.75).sample(bqm, ... num_reads=1000).first.energy 3.5
Properties¶
The child sampler. 

List of child samplers that that are used by this composite. 

A dict containing any additional information about the sampler. 

A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevent to each parameter. 
Methods¶

Cut off interactions and sample from the provided binary quadratic model. 

Sample from an Ising model using the implemented sample method. 

Sample from a QUBO using the implemented sample method. 
PolyCutOffComposite¶
Prunes the polynomial submitted to the child sampler by retaining only interactions with values commensurate with the sampler’s precision.
 class PolyCutOffComposite(child_sampler, cutoff, cutoff_vartype=Vartype.SPIN, comparison=<builtin function lt>)[source]¶
Composite to remove polynomial interactions below a specified cutoff value.
Prunes the binary polynomial submitted to the child sampler by retaining only interactions with values commensurate with the sampler’s precision as specified by the
cutoff
argument. Also removes variables isolated post or preremoval of these interactions from the polynomial passed on to the child sampler, setting these variables to values that minimize the original polynomial’s energy for the returned samples. Parameters
sampler (
dimod.PolySampler
) – A dimod binary polynomial sampler.cutoff (number) – Lower bound for absolute value of interactions. Interactions with absolute values lower than
cutoff
are removed. Isolated variables are also not passed on to the child sampler.cutoff_vartype (
Vartype
/str/set, default=’SPIN’) –Variable space to do the cutoff in. Accepted input values:
Vartype.SPIN
,'SPIN'
,{1, 1}
Vartype.BINARY
,'BINARY'
,{0, 1}
comparison (function, optional) – A comparison operator for comparing the interaction value to the cutoff value. Defaults to
operator.lt()
.
Examples
This example removes one interaction,
'ac': 0.2
, before submitting the polynomial to child samplerExactSolver
.>>> import dimod >>> sampler = dimod.HigherOrderComposite(dimod.ExactSolver()) >>> poly = dimod.BinaryPolynomial({'a': 3, 'abc':4, 'ac': 0.2}, dimod.SPIN) >>> PolyCutOffComposite(sampler, 1).sample_poly(poly).first.sample['a'] 1
Properties¶
The child sampler. 

List of child samplers that that are used by this composite. 

A dict containing any additional information about the sampler. 

A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevent to each parameter. 
Methods¶

Cutoff and sample from the provided binary polynomial. 

Sample from a higherorder Ising model. 

Sample from a higherorder unconstrained binary optimization problem. 
Embedding¶
Minorembed a problem BQM into a DWave system.
Embedding composites for various types of problems and application. For example:
EmbeddingComposite
for a problem with arbitrary structure that likely requires hueristic embedding.AutoEmbeddingComposite
can save unnecessary embedding for problems that might have a structure similar to the child sampler.LazyFixedEmbeddingComposite
can benefit applications that resubmit a BQM with changes in some values.
AutoEmbeddingComposite¶
 class AutoEmbeddingComposite(child_sampler, **kwargs)[source]¶
Maps problems to a structured sampler, embedding if needed.
This composite first tries to submit the binary quadratic model directly to the child sampler and only embeds if a
dimod.exceptions.BinaryQuadraticModelStructureError
is raised. Parameters
child_sampler (
dimod.Sampler
) – Structured dimod sampler, such as aDWaveSampler()
.find_embedding (function, optional) – A function find_embedding(S, T, **kwargs) where S and T are edgelists. The function can accept additional keyword arguments. Defaults to
minorminer.find_embedding()
.kwargs – See the
EmbeddingComposite
class for additional keyword arguments.
Properties¶
The child sampler. 

Parameters in the form of a dict. 

Properties in the form of a dict. 
Methods¶

Sample from the provided binary quadratic model. 

Sample from an Ising model using the implemented sample method. 
Sample from a QUBO using the implemented sample method. 
EmbeddingComposite¶
 class EmbeddingComposite(child_sampler, find_embedding=<function find_embedding>, embedding_parameters=None, scale_aware=False, child_structure_search=<function child_structure_dfs>)[source]¶
Maps problems to a structured sampler.
Automatically minorembeds a problem into a structured sampler such as a DWave system. A new minorembedding is calculated each time one of its sampling methods is called.
 Parameters
child_sampler (
dimod.Sampler
) – A dimod sampler, such as aDWaveSampler
, that accepts only binary quadratic models of a particular structure.find_embedding (function, optional) – A function find_embedding(S, T, **kwargs) where S and T are edgelists. The function can accept additional keyword arguments. Defaults to
minorminer.find_embedding()
.embedding_parameters (dict, optional) – If provided, parameters are passed to the embedding method as keyword arguments.
scale_aware (bool, optional, default=False) – Pass chain interactions to child samplers that accept an ignored_interactions parameter.
child_structure_search (function, optional) – A function child_structure_search(sampler) that accepts a sampler and returns the
dimod.Structured.structure
. Defaults todimod.child_structure_dfs()
.
Examples
>>> from dwave.system import DWaveSampler, EmbeddingComposite ... >>> sampler = EmbeddingComposite(DWaveSampler()) >>> h = {'a': 1., 'b': 2} >>> J = {('a', 'b'): 1.5} >>> sampleset = sampler.sample_ising(h, J, num_reads=100) >>> sampleset.first.energy 4.5
Properties¶
The child sampler. 

Parameters in the form of a dict. 

Properties in the form of a dict. 

Defines the default behaviour for 

Defines the default behavior for 
Methods¶

Sample from the provided binary quadratic model. 

Sample from an Ising model using the implemented sample method. 

Sample from a QUBO using the implemented sample method. 
FixedEmbeddingComposite¶
 class FixedEmbeddingComposite(child_sampler, embedding=None, source_adjacency=None, **kwargs)[source]¶
Maps problems to a structured sampler with the specified minorembedding.
 Parameters
child_sampler (dimod.Sampler) – Structured dimod sampler such as a DWave system.
embedding (dict[hashable, iterable], optional) – Mapping from a source graph to the specified sampler’s graph (the target graph).
source_adjacency (dict[hashable, iterable]) – Deprecated. Dictionary to describe source graph as {node: {node neighbours}}.
kwargs – See the
EmbeddingComposite
class for additional keyword arguments. Note thatfind_embedding
andembedding_parameters
keyword arguments are ignored.
Examples
To embed a triangular problem (a problem with a threenode complete graph, or clique) in the Chimera topology, you need to chain two qubits. This example maps triangular problems to a composed sampler (based on the unstructured
ExactSolver
) with a Chimera unitcell structure.>>> import dimod >>> import dwave_networkx as dnx >>> from dwave.system import FixedEmbeddingComposite ... >>> c1 = dnx.chimera_graph(1) >>> embedding = {'a': [0, 4], 'b': [1], 'c': [5]} >>> structured_sampler = dimod.StructureComposite(dimod.ExactSolver(), ... c1.nodes, c1.edges) >>> sampler = FixedEmbeddingComposite(structured_sampler, embedding) >>> sampler.edgelist [('a', 'b'), ('a', 'c'), ('b', 'c')]
Properties¶
Properties in the form of a dict. 

Parameters in the form of a dict. 

List containing the structured sampler. 

The child sampler. 

Nodes available to the composed sampler. 

Edges available to the composed sampler. 

Adjacency structure for the composed sampler. 

Structure of the structured sampler formatted as a 
Methods¶

Sample the binary quadratic model. 

Sample from an Ising model using the implemented sample method. 
Sample from a QUBO using the implemented sample method. 
LazyFixedEmbeddingComposite¶
 class LazyFixedEmbeddingComposite(child_sampler, find_embedding=<function find_embedding>, embedding_parameters=None, scale_aware=False, child_structure_search=<function child_structure_dfs>)[source]¶
Maps problems to the structure of its first given problem.
This composite reuses the minorembedding found for its first given problem without recalculating a new minorembedding for subsequent calls of its sampling methods.
 Parameters
child_sampler (dimod.Sampler) – Structured dimod sampler.
find_embedding (function, default=:func:minorminer.find_embedding) – A function find_embedding(S, T, **kwargs) where S and T are edgelists. The function can accept additional keyword arguments. The function is used to find the embedding for the first problem solved.
embedding_parameters (dict, optional) – If provided, parameters are passed to the embedding method as keyword arguments.
Examples
>>> from dwave.system import LazyFixedEmbeddingComposite, DWaveSampler ... >>> sampler = LazyFixedEmbeddingComposite(DWaveSampler()) >>> sampler.nodelist is None # no structure prior to first sampling True >>> __ = sampler.sample_ising({}, {('a', 'b'): 1}) >>> sampler.nodelist # has structure based on given problem ['a', 'b']
Properties¶
Parameters in the form of a dict. 

Properties in the form of a dict. 

Nodes available to the composed sampler. 

Edges available to the composed sampler. 

Adjacency structure for the composed sampler. 

Structure of the structured sampler formatted as a 
Methods¶

Sample the binary quadratic model. 
Sample from an Ising model using the implemented sample method. 

Sample from a QUBO using the implemented sample method. 
TilingComposite¶
 class TilingComposite(sampler, sub_m, sub_n, t=4)[source]¶
Composite to tile a small problem across a structured sampler.
Enables parallel sampling on Chimera or Pegasus structured samplers of small problems. The small problem should be defined on a Chimera graph of dimensions
sub_m
,sub_n
,t
, or minorembeddable to such a graph.Notation CN refers to a Chimera graph consisting of an NxN grid of unit cells, where each unit cell is a bipartite graph with shores of size t. The DWave 2000Q QPU supports a C16 Chimera graph: its 2048 qubits are logically mapped into a 16x16 matrix of unit cells of 8 qubits (t=4). See also :func:dwave_networkx.chimera_graph
Notation PN referes to a Pegasus graph consisting of a 3x(N1)x(N1) grid of cells, where each unit cell is a bipartite graph with shore of size t, supplemented with odd couplers (see nice_coordinate definition). The Advantage QPU supports a P16 Pegasus graph: its qubits may be mapped to a 3x15x15 matrix of unit cells, each of 8 qubits. This code supports tiling of Chimerastructured problems, with an option of additional oddcouplers, onto Pegasus. See also :func:dwave_networkx.pegasus_graph .
A problem that can be minorembedded in a single chimera unit cell, for example, can therefore be tiled across the unit cells of a DWave 2000Q as 16x16 duplicates (or Advantage as 3x15x15 duplicates), subject to solver yield. This enables up to 256 (625) parallel samples per read.
 Parameters
sampler (
dimod.Sampler
) – Structured dimod sampler such as aDWaveSampler()
.sub_m (int) – Minimum number of Chimera unit cell rows required for minorembedding a single instance of the problem.
sub_n (int) – Minimum number of Chimera unit cell columns required for minorembedding a single instance of the problem.
t (int, optional, default=4) – Size of the shore within each Chimera unit cell.
Examples
This example submits a twovariable QUBO problem representing a logical NOT gate to a DWave system. The QUBO—two nodes with biases of 1 that are coupled with strength 2—needs only any two coupled qubits and so is easily minorembedded in a single unit cell. Composite
TilingComposite
tiles it multiple times for parallel solution: the two nodes should typically have opposite values.>>> from dwave.system import DWaveSampler, EmbeddingComposite >>> from dwave.system import TilingComposite ... >>> qpu_2000q = DWaveSampler(solver={'topology__type': 'chimera'}) >>> sampler = EmbeddingComposite(TilingComposite(qpu_2000q, 1, 1, 4)) >>> Q = {(1, 1): 1, (1, 2): 2, (2, 1): 0, (2, 2): 1} >>> sampleset = sampler.sample_qubo(Q) >>> len(sampleset)> 1 True
See Ocean Glossary for explanations of technical terms in descriptions of Ocean tools.
Properties¶
Properties in the form of a dict. 

Parameters in the form of a dict. 

The single wrapped structured sampler. 

The child sampler. 

List of active qubits for the structured solver. 

List of active couplers for the DWave solver. 

Adjacency structure formatted as a dict, where keys are the nodes of the structured sampler and values are sets of all adjacent nodes for each key node. 

Structure of the structured sampler formatted as a 
Methods¶

Sample from the specified binary quadratic model. 

Sample from an Ising model using the implemented sample method. 

Sample from a QUBO using the implemented sample method. 
VirtualGraphComposite¶
 class VirtualGraphComposite(sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600)[source]¶
Composite to use the DWave virtual graph feature for minorembedding.
Calibrates qubits in chains to compensate for the effects of biases and enables easy creation, optimization, use, and reuse of an embedding for a given working graph.
Inherits from
dimod.ComposedSampler
anddimod.Structured
. Parameters
sampler (
DWaveSampler
) – A dimoddimod.Sampler
. Typically aDWaveSampler
or derived composite sampler; other samplers may not work or make sense with this composite layer.embedding (dict[hashable, iterable]) – Mapping from a source graph to the specified sampler’s graph (the target graph).
chain_strength (float, optional, default=None) – Desired chain coupling strength. This is the magnitude of couplings between qubits in a chain. If None, uses the maximum available as returned by a SAPI query to the DWave solver.
flux_biases (list/False/None, optional, default=None) – Perqubit flux bias offsets in the form of a list of lists, where each sublist is of length 2 and specifies a variable and the flux bias offset associated with that variable. Qubits in a chain with strong negative J values experience a Jinduced bias; this parameter compensates by recalibrating to remove that bias. If False, no flux bias is applied or calculated. If None, flux biases are pulled from the database or calculated empirically.
flux_bias_num_reads (int, optional, default=1000) – Number of samples to collect per flux bias value to calculate calibration information.
flux_bias_max_age (int, optional, default=3600) – Maximum age (in seconds) allowed for a previously calculated flux bias offset to be considered valid.
Attention
DWave’s virtual graphs feature can require many seconds of DWave system time to calibrate qubits to compensate for the effects of biases. If your account has limited DWave system access, consider using
FixedEmbeddingComposite
instead.Examples
This example uses
VirtualGraphComposite
to instantiate a composed sampler that submits a QUBO problem to a DWave solver. The problem represents a logical AND gate using penalty function \(P = xy  2(x+y)z +3z\), where variables x and y are the gate’s inputs and z the output. This simple threevariable problem is manually minorembedded to a single Chimera unit cell: variables x and y are represented by qubits 1 and 5, respectively, and z by a twoqubit chain consisting of qubits 0 and 4. The chain strength is set to the maximum allowed found from querying the solver’s extended J range. In this example, the ten returned samples all represent valid states of the AND gate.>>> from dwave.system import DWaveSampler, VirtualGraphComposite >>> embedding = {'x': {1}, 'y': {5}, 'z': {0, 4}} >>> qpu_2000q = DWaveSampler(solver={'topology__type': 'chimera'}) >>> qpu_2000q.properties['extended_j_range'] [2.0, 1.0] >>> sampler = VirtualGraphComposite(qpu_2000q, embedding, chain_strength=2) >>> Q = {('x', 'y'): 1, ('x', 'z'): 2, ('y', 'z'): 2, ('z', 'z'): 3} >>> sampleset = sampler.sample_qubo(Q, num_reads=10) >>> print(sampleset) x y z energy num_oc. chain_. 0 1 0 0 0.0 2 0.0 1 0 1 0 0.0 3 0.0 2 1 1 1 0.0 3 0.0 3 0 0 0 0.0 2 0.0 ['BINARY', 4 rows, 10 samples, 3 variables]
See Ocean Glossary for explanations of technical terms in descriptions of Ocean tools.
Properties¶
Properties in the form of a dict. 

Parameters in the form of a dict. 

List containing the structured sampler. 

The child sampler. 

Nodes available to the composed sampler. 

Edges available to the composed sampler. 

Adjacency structure for the composed sampler. 

Structure of the structured sampler formatted as a 
Methods¶

Sample from the given Ising model. 

Sample from an Ising model using the implemented sample method. 
Sample from a QUBO using the implemented sample method. 
Reverse Anneal¶
Composites that do batch operations for reverse annealing based on sets of initial states or anneal schedules.
ReverseBatchStatesComposite¶
 class ReverseBatchStatesComposite(child_sampler)[source]¶
Composite that reverse anneals from multiple initial samples. Each submission is independent from one another.
 Parameters
sampler (
dimod.Sampler
) – A dimod sampler.
Examples
This example runs 100 reverse anneals each from two initial states on a problem constructed by setting random \(\pm 1\) values on a clique (complete graph) of 15 nodes, minorembedded on a DWave system using the
DWaveCliqueSampler
sampler.>>> import dimod >>> from dwave.system import DWaveCliqueSampler, ReverseBatchStatesComposite ... >>> sampler = DWaveCliqueSampler() >>> sampler_reverse = ReverseBatchStatesComposite(sampler) >>> schedule = [[0.0, 1.0], [10.0, 0.5], [20, 1.0]] ... >>> bqm = dimod.generators.ran_r(1, 15) >>> init_samples = [{i: 1 for i in range(15)}, {i: 1 for i in range(15)}] >>> sampleset = sampler_reverse.sample(bqm, ... anneal_schedule=schedule, ... initial_states=init_samples, ... num_reads=100, ... reinitialize_state=True)
Properties¶
The child sampler. 

List of child samplers that that are used by this composite. 

A dict containing any additional information about the sampler. 

A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevent to each parameter. 
Methods¶

Sample the binary quadratic model using reverse annealing from multiple initial states. 
Sample from an Ising model using the implemented sample method. 

Sample from a QUBO using the implemented sample method. 
ReverseAdvanceComposite¶
 class ReverseAdvanceComposite(child_sampler)[source]¶
Composite that reverse anneals an initial sample through a sequence of anneal schedules.
If you do not specify an initial sample, a random sample is used for the first submission. By default, each subsequent submission selects the mostfound lowestenergy sample as its initial state. If you set reinitialize_state to False, which makes each submission behave like a random walk, the subsequent submission selects the last returned sample as its initial state.
 Parameters
sampler (
dimod.Sampler
) – A dimod sampler.
Examples
This example runs 100 reverse anneals each for three schedules on a problem constructed by setting random \(\pm 1\) values on a clique (complete graph) of 15 nodes, minorembedded on a DWave system using the
DWaveCliqueSampler
sampler.>>> import dimod >>> from dwave.system import DWaveCliqueSampler, ReverseAdvanceComposite ... >>> sampler = DWaveCliqueSampler() >>> sampler_reverse = ReverseAdvanceComposite(sampler) >>> schedule = [[[0.0, 1.0], [t, 0.5], [20, 1.0]] for t in (5, 10, 15)] ... >>> bqm = dimod.generators.ran_r(1, 15) >>> init_samples = {i: 1 for i in range(15)} >>> sampleset = sampler_reverse.sample(bqm, ... anneal_schedules=schedule, ... initial_state=init_samples, ... num_reads=100, ... reinitialize_state=True)
Properties¶
The child sampler. 

List of child samplers that that are used by this composite. 

A dict containing any additional information about the sampler. 

A dict where keys are the keyword parameters accepted by the sampler methods and values are lists of the properties relevent to each parameter. 
Methods¶

Sample the binary quadratic model using reverse annealing along a given set of anneal schedules. 

Sample from an Ising model using the implemented sample method. 
Sample from a QUBO using the implemented sample method. 