Samplers and Composites¶
Samplers are processes that sample from low energy states of a problem’s objective function. A BQM sampler samples from low energy states in models such as those defined by an Ising equation or a Quadratic Unconstrained Binary Optimization (QUBO) problem and returns an iterable of samples, in order of increasing energy.
Ocean software provides a variety of dimod samplers, which
all support ‘sample_qubo’ and ‘sample_ising’ methods as well as the generic BQM sampler method.
In addition to
DWaveSampler(), classical solvers, which run on CPU or GPU, are available and
useful for developing code or on a small versions of a problem to verify code.
Hybrid Quantum-Classical Samplers¶
Quantum-classical hybrid is the use of both classical and quantum resources to solve problems, exploiting the complementary strengths that each provides.
D-Wave’s Leap Quantum Application Environment provides state-of-the-art hybrid solvers you can submit arbitrary BQMs to. dwave-hybrid provides you with a Python framework for building a variety of flexible hybrid workflows that use quantum and classical resources together to find good solutions to your problem.
Ocean software provides quantum, classical, and quantum-classical hybrid samplers that run either remotely (for example, in D-Wave’s Leap environment) or locally on your CPU. These compute resources are known as solvers.
Some classical samplers actually brute-force solve small problems rather than sample, and these are also referred to as “solvers”.
Samplers can be composed. The composite pattern allows layers of pre- and post-processing to be applied to binary quadratic programs without needing to change the underlying sampler implementation. We refer to these layers as composites. A composed sampler includes at least one sampler and possibly many composites.
Examples of composites are
which handle the mapping known as minor-embedding,
uses roof duality to assign
some variables as a pre-processing step before submitting the problem for sampling.
The use of samplers in solving problems is described in the following documentation: