Concepts¶
See the Glossary for short definitions of terminology or learn Ocean concepts here:
Concepts 
Related terms 

BQM, Ising, QUBO 

CSP, binary CSP 

DQM, discrete quadratic model 

quantumclassical hybrid, Leap’s hybrid solvers, hybrid workflows 

embedding, mapping logical variables to physical qubits, chains, chain strength 

Chimera, Pegasus 

solver 

samples, sampleset, probabilistic, energy 
Glossary¶
 binary quadratic model
 BQM
A collection of binaryvalued variables (variables that can be assigned two values, for example 1, 1) with associated linear and quadratic biases. Sometimes referred to in other tools as a problem. See a fuller description under Binary Quadratic Models.
 Chain
One or more nodes or qubits in a target graph that represent a single variable in the source graph. See embedding. See a fuller description under MinorEmbedding.
 Chain length
The number of qubits in a Chain. See a fuller description under MinorEmbedding.
 Chain strength
Magnitude of the negative quadratic bias applied between variables to form a chain. See a fuller description under MinorEmbedding.
 Chimera
The DWave QPU is a lattice of interconnected qubits. While some qubits connect to others via couplers, the DWave QPU is not fully connected. Instead, the qubits interconnect in an architecture known as Chimera. See a fuller description under QPU Topology.
 Complete graph
 Fully connected
See complete graph. on wikipedia. A fully connected or complete binary quadratic model is one that has interactions between all of its variables.
 Composed sampler
Samplers that apply pre and/or postprocessing to binary quadratic programs without changing the underlying sampler implementation by layering composite patterns on the sampler. For example, a composed sampler might add spin transformations when sampling from the DWave system.
 Composite
A sampler can be composed. The composite pattern allows layers of pre and postprocessing 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.
 CSP
Constraint satisfaction problem. A constraint satisfaction problem (CSP) requires that all the problem’s variables be assigned values, out of a finite domain, that result in the satisfying of all constraints. See a fuller description under QPU Topology.
 discrete quadratic model
 DQM
A collection of discretevalued variables (variables that can be assigned the values specified by a set such as \(\{red, green, blue\}\) or \(\{33, 5.7, 3,14 \}\) ) with associated linear and quadratic biases. See a fuller description under Discrete Quadratic Models.
 Embed
 Embedding
 Minorembed
 Minorembedding
The nodes and edges on the graph that represents an objective function translate to the qubits and couplers in Chimera. Each logical qubit, in the graph of the objective function, may be represented by one or more physical qubits. The process of mapping the logical qubits to physical qubits is known as minor embedding. See a fuller description under MinorEmbedding.
 Excited state
States of a quantum system that have higher energy than the ground state. Such states represent nonoptimal solutions for problems represented by an Objective function and infeasible configurations for problems represented by a penalty model.
 Graph
A collection of nodes and edges. A graph can be derived from a model: a node for each variable and an edge for each pair of variables with a nonzero quadratic bias.
 Ground state
The lowestenergy state of a quantummechanical system and the global minimum of a problem represented by an Objective function.
 Hamiltonian
A classical Hamiltonian is a mathematical description of some physical system in terms of its energies. We can input any particular state of the system, and the Hamiltonian returns the energy for that state. For a quantum system, a Hamiltonian is a function that maps certain states, called eigenstates, to energies. Only when the system is in an eigenstate of the Hamiltonian is its energy well defined and called the eigenenergy. When the system is in any other state, its energy is uncertain. For the DWave system, the Hamiltonian may be represented as
\begin{equation} {\cal H}_{ising} = \underbrace{\frac{A({s})}{2} \left(\sum_i {\hat\sigma_{x}^{(i)}}\right)}_\text{Initial Hamiltonian} + \underbrace{\frac{B({s})}{2} \left(\sum_{i} h_i {\hat\sigma_{z}^{(i)}} + \sum_{i>j} J_{i,j} {\hat\sigma_{z}^{(i)}} {\hat\sigma_{z}^{(j)}}\right)}_\text{Final Hamiltonian} \end{equation}where \({\hat\sigma_{x,z}^{(i)}}\) are Pauli matrices operating on a qubit \(q_i\), and \(h_i\) and \(J_{i,j}\) are the qubit biases and coupling strengths.
 Hardware graph
See hardware graph. The hardware graph is the physical lattice of interconnected qubits. See also working graph. See a fuller description under QPU Topology.
 Hybrid
Quantumclassical hybrid is the use of both classical and quantum resources to solve problems, exploiting the complementary strengths that each provides. See Hybrid Solvers.
 Ising
Traditionally used in statistical mechanics. Variables are “spin up” (\(\uparrow\)) and “spin down” (\(\downarrow\)), states that correspond to \(+1\) and \(1\) values. Relationships between the spins, represented by couplings, are correlations or anticorrelations. The objective function expressed as an Ising model is as follows:
\begin{equation} \text{E}_{ising}(\pmb{s}) = \sum_{i=1}^N h_i s_i + \sum_{i=1}^N \sum_{j=i+1}^N J_{i,j} s_i s_j \end{equation}where the linear coefficients corresponding to qubit biases are \(h_i\), and the quadratic coefficients corresponding to coupling strengths are \(J_{i,j}\). See also Ising Model on Wikipedia.
 Minimum gap
The minimum distance between the ground state and the first excited state throughout any point in the anneal.
 Model
A collection of variables with associated linear and quadratic biases. Sometimes referred to as a problem.
 Objective function
A mathematical expression of the energy of a system as a function of binary variables representing the qubits.
 Pegasus
The DWave QPU is a lattice of interconnected qubits. While some qubits connect to others via couplers, the DWave QPU is not fully connected. Instead, the qubits interconnect in an architecture known as Pegasus. See a fuller description under QPU Topology.
 Penalty function
An algorithm for solving constrained optimization problems. In the context of Ocean tools, penalty functions are typically employed to increase the energy level of a problem’s objective function by penalizing nonvalid configurations. See Penalty method on Wikipedia
 Penalty model
An approach to solving constraint satisfaction problems (CSP) using an Ising model or a QUBO by mapping each individual constraint in the CSP to a ‘small’ Ising model or QUBO.
 QPU
Quantum processing unit.
 QUBO
Quadratic unconstrained binary optimization. QUBO problems are traditionally used in computer science. Variables are TRUE and FALSE, states that correspond to 1 and 0 values. A QUBO problem is defined using an upperdiagonal matrix \(Q\), which is an \(N\) x \(N\) uppertriangular matrix of real weights, and \(x\), a vector of binary variables, as minimizing the function
\begin{equation} f(x) = \sum_{i} {Q_{i,i}}{x_i} + \sum_{i<j} {Q_{i,j}}{x_i}{x_j} \end{equation}where the diagonal terms \(Q_{i,i}\) are the linear coefficients and the nonzero offdiagonal terms are the quadratic coefficients \(Q_{i,j}\). This can be expressed more concisely as
\begin{equation} \min_{{x} \in {\{0,1\}^n}} {x}^{T} {Q}{x}. \end{equation}In scalar notation, the objective function expressed as a QUBO is as follows:
\begin{equation} \text{E}_{qubo}(a_i, b_{i,j}; q_i) = \sum_{i} a_i q_i + \sum_{i<j} b_{i,j} q_i q_j. \end{equation}See also QUBO on Wikipedia.
 Sampler
Samplers are processes that sample from low energy states of a problem’s objective function, which is a mathematical expression of the energy of a system. A binary quadratic model (BQM) sampler samples from low energy states in models such as those defined by an Ising equation or a QUBO problem and returns an iterable of samples, in order of increasing energy.
 SAPI
Solver API used by clients to communicate with a solver.
 Solver
A resource that runs a problem. Some solvers interface to the QPU; others leverage CPU and GPU resources.
 Source
 Source graph
In the context of embedding, the model or induced graph that we wish to embed. Sometimes referred to as the logical graph/model. See a fuller description under MinorEmbedding.
 Structured sampler
Samplers that are restricted to sampling only binary quadratic models defined on a specific graph.
 Subgraph
See subgraph on wikipedia.
 Target
 Target graph
Embedding attempts to create a target model from a target graph. The process of embedding takes a source model, derives the source graph, maps the source graph to the target graph, then derives the target model. Sometimes referred to as the embedded graph/model. See a fuller description under MinorEmbedding.
 Working graph
In a DWave QPU, the set of qubits and couplers that are available for computation is known as the working graph. The yield of a working graph is typically less than 100% of qubits and couplers that are fabricated and physically present in the QPU. See hardware graph.