Generators and Application Modeling

Benchmarking

anti_crossing_clique(num_variables)	Generate an anti-crossing problem with a single clique.
anti_crossing_loops(num_variables)	Generate an anti-crossing problem with two loops.
chimera_anticluster(m[, n, t, multiplier, ...])	Generate an anticluster problem on a Chimera lattice.
doped(p, graph[, cls, seed, fm])	Generate a BQM for a doped ferromagnetic (FM) or antiferromagnetic (AFM) problem.
frustrated_loop(graph, num_cycles[, R, ...])	Generate a frustrated-loop problem.
random_2in4sat(variables, num_clauses, *[, ...])	Generate a random 2-in-4 satisfiability problem as a binary quadratic model.
random_nae3sat(variables, num_clauses, *[, ...])	Generate a random not-all-equal 3-satisfiability problem as a binary quadratic model.

Constraints

and_gate(in0, in1, out, *[, strength])	Generate a binary quadratic model with ground states corresponding to an AND gate.
binary_encoding(v, upper_bound)	Generate a binary quadratic model encoding an integer.
combinations(n, k[, strength, vartype])	Generate a binary quadratic model that is minimized when k of n variables are selected.
fulladder_gate(in0, in1, in2, sum_, carry, *)	Generate a binary quadratic model with ground states corresponding to a full adder gate.
halfadder_gate(in0, in1, sum_, carry, *[, ...])	Generate a binary quadratic model with ground states corresponding to a half adder gate.
multiplication_circuit(num_arg1_bits[, ...])	Generate a binary quadratic model with ground states corresponding to a multiplication circuit.
or_gate(in0, in1, out, *[, strength])	Generate a binary quadratic model with ground states corresponding to an OR gate.
xor_gate(in0, in1, out, aux, *[, strength])	Generate a binary quadratic model with ground states corresponding to an XOR gate.

Optimization

independent_set(edges[, nodes])	Generate a binary quadratic model encoding an independent set problem.
maximum_independent_set(edges[, nodes, strength])	Generate a binary quadratic model encoding a maximum independent set problem.
maximum_weight_independent_set(edges[, ...])	Generate a binary quadratic model encoding a maximum-weight independent set problem.
random_bin_packing(num_items[, seed, ...])	Generate a bin packing problem as a constrained quadratic model.
random_knapsack(num_items[, seed, ...])	Generates a constrained quadratic model encoding a knapsack problem.
random_multi_knapsack(num_items, num_bins[, ...])	Generate a constrained quadratic model encoding a multiple-knapsack problem.

Random

doped(p, graph[, cls, seed, fm])	Generate a BQM for a doped ferromagnetic (FM) or antiferromagnetic (AFM) problem.
gnm_random_bqm(variables, num_interactions, ...)	Generate a random binary quadratic model with a fixed number of variables and interactions.
gnp_random_bqm(n, p, vartype[, cls, ...])	Generate a BQM structured as an Erdős-Rényi graph.
randint(graph, vartype[, low, high, cls, seed])	Generate a binary quadratic model with random biases and offset.
random_2in4sat(variables, num_clauses, *[, ...])	Generate a random 2-in-4 satisfiability problem as a binary quadratic model.
random_bin_packing(num_items[, seed, ...])	Generate a bin packing problem as a constrained quadratic model.
random_knapsack(num_items[, seed, ...])	Generates a constrained quadratic model encoding a knapsack problem.
random_multi_knapsack(num_items, num_bins[, ...])	Generate a constrained quadratic model encoding a multiple-knapsack problem.
random_nae3sat(variables, num_clauses, *[, ...])	Generate a random not-all-equal 3-satisfiability problem as a binary quadratic model.
ran_r(r, graph[, cls, seed])	Generate an Ising model for a RANr problem.
uniform(graph, vartype[, low, high, cls, seed])	Generate a binary quadratic model with random biases and offset.

Single-Variable Models

Generators for single-variable models used in symbolic math.

Binary([label, bias, dtype])	Return a binary quadratic model with a single binary variable.
Binaries(labels[, dtype])	Yield binary quadratic models, each with a single binary variable.
BinaryArray(labels[, dtype])	Return a NumPy array of binary quadratic models, each with a single binary variable.
Integer([label, bias, dtype, lower_bound, ...])	Return a quadratic model with a single integer variable.
Integers(labels[, dtype])	Yield quadratic models, each with a single integer variable.
IntegerArray(labels[, dtype])	Return a NumPy array of quadratic models, each with a single integer variable.
Spin([label, bias, dtype])	Return a binary quadratic model with a single spin variable.
Spins(labels[, dtype])	Yield binary quadratic models, each with a single spin variable.
SpinArray(labels[, dtype])	Return a NumPy array of binary quadratic models, each with a single spin variable.