Concepts#

See the Glossary for short definitions of terminology or learn Ocean concepts here:

Ocean Concepts#
Concepts	Related terms
Binary Quadratic Models	BQM, Ising, QUBO
Constrained Quadratic Models	CQM, constrained quadratic model
Constraint Satisfaction	CSP, binary CSP
Discrete Quadratic Models	DQM, discrete quadratic model
Hybrid	quantum-classical hybrid, Leap’s hybrid solvers, hybrid workflows
Minor-Embedding	embedding, mapping logical variables to physical qubits, chains, chain strength
Nonlinear Models	nonlinear model, non-linear model, NL model
Penalty Models	Penalty Models
Quadratic Models	Quadratic Models
QPU Topology	Chimera, Pegasus
Samplers and Composites	solver
Solutions	samples, sampleset, probabilistic, energy
Variables	binary, discrete, integer, real variables

Glossary#

binary quadratic model#

BQM#

A collection of binary-valued 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 Minor-Embedding.

Chain length#

The number of qubits in a Chain. See a fuller description under Minor-Embedding.

Chain strength#

Magnitude of the negative quadratic bias applied between variables to form a chain. See a fuller description under Minor-Embedding.

Chimera#

A D-Wave QPU is a lattice of interconnected qubits. While some qubits connect to others via couplers, D-Wave QPUs are not fully connected. For D-Wave 2000Q QPUs, the qubits interconnect in an architecture known as Chimera. See a fuller description under QPU Topology. See also Pegasus and Zephyr.

Clique#

Complete graph#

Fully connected#

See complete graph on Wikipedia or Clique. 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 post-processing 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 D-Wave system.

Composite#

A sampler 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.

Connected graph#

See Connected graph on the US NIST site. A connected graph has some path from any vertex to any other. A graph that has at least two vertices without a path between them is disconnected. Any Complete graph is connected (but not all connected graphs are complete).

Constrained quadratic model#

CQM#

A collection of variables with associated linear and quadratic biases representing a problem modeled as an objective function and inequality and equality constraints.

See a fuller description under Constrained Quadratic Models.

Constraint satisfaction problem#

CSP#

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 discrete-valued 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#

Minor-embed#

Minor-embedding#

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 Minor-Embedding.

Excited state#

States of a quantum system that have higher energy than the ground state. Such states represent non-optimal 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 non-zero quadratic bias.

Ground state#

The lowest-energy state of a quantum-mechanical 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 D-Wave 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#

Quantum-classical 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 anti-correlations. 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 biases. Sometimes referred to as a problem.

Objective function#

A mathematical expression of the energy of a system as a function of its variables.

Nonlinear model#

A collection of variables with associated biases that constitute an objective function and/or constraints. Sometimes referred to as a problem. See a fuller description under Nonlinear Models.

Pegasus#

A D-Wave QPU is a lattice of interconnected qubits. While some qubits connect to others via couplers, D-Wave QPUs are not fully connected. For an Advantage QPU, the qubits interconnect in an architecture known as Pegasus. See a fuller description under QPU Topology. See also Chimera and Zephyr.

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 non-valid 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.

Quadratic model#

A collection of variables with associated linear and quadratic biases. Sometimes referred to as a problem.

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 upper-diagonal matrix \(Q\), which is an \(N\) x \(N\) upper-triangular 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 off-diagonal 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}