Pau Farré

Allow DWaveCliqueSampler to use a user specified clique embedding

2

I'm interested in using all the functionality of the clique sampler (range-scaled uniform torque compensation, per-qubit coupling checks), but I would like the option of passing my own embedding instead...

Track how long do embedding methods take

1

It would be great if when an `EmbeddingComposite` is used, the `sampleset.info["timing"]` dictionary was updated with the `EmbeddingComposite` overheads. The thing that I'm interested the most for benchmarking purposes is...

Allow composite samplers to expose the innermost child sampler, this can be used to gather properties. For example, one can get QPU properties from a composite made of several layers

Obtaining basic metadata from dimod models

3

**Application** Dimod-supported characterization the object properties **Proposed Solution** For each of the canonical models, eg. BQM, DQM, there could be a method that puts together some of the model properties,...

This composite generalizes making multiple calls to a sampler and aggregating the results together. On each call, one specifies a different set of kwargs to be sent to the solver....

bqm.to_coo() not storing the offset

3

**Description** bqm offsets are not appearing in .coo files **Steps To Reproduce** ``` import itertools import dimod bqm = dimod.BQM.from_ising({}, {c: 1 for c in itertools.combinations(range(4), 2)}, offset=42) with open("inputs/{}.coo".format("test_1"),...

`load_cnf` returns no constraints from CNF file

1

**Description** I'm reading a CNF file with `load_cnf`, but the function returns a csp object without constraints **Steps To Reproduce** ``` In [1]: import dwavebinarycsp as dbcsp In [2]: cnf_file...

add power-r generator

3

Weights follow a power-law distribution with respect to their magnitude. Good for high precision instances.

add LinearAncillaComposite

1

In scenarios where the h biases are not available or sufficient in range, one can use auxiliary qubits polarized with a large flux bias and coupled to the data qubit....

embed_bqm silently assumes that chain_strength is a positive number

3

https://github.com/dwavesystems/dwave-system/blob/b233941673cda08c194d769b067ab06ce43ea077/dwave/embedding/transforms.py#L274-L278 `embed_bqm` assumes a positive sign for `chain_strength`: ```python import networkx as nx from dwave.embedding import embed_bqm import dimod g = nx.Graph() g.add_edge(100, 101) bqm = dimod.BQM.from_ising({1: 0}, {}) emb...