Generators and Application Modeling¶
Benchmarking¶
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Generate an anti-crossing problem with a single clique. |
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Generate an anti-crossing problem with two loops. |
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Generate an anticluster problem on a Chimera lattice. |
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Generate a BQM for a doped ferromagnetic (FM) or antiferromagnetic (AFM) problem. |
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Generate a frustrated-loop problem. |
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Generate a random 2-in-4 satisfiability problem as a binary quadratic model. |
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Generate a random not-all-equal 3-satisfiability problem as a binary quadratic model. |
Constraints¶
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Generate a binary quadratic model with ground states corresponding to an AND gate. |
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Generate a binary quadratic model encoding an integer. |
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Generate a binary quadratic model that is minimized when |
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Generate a binary quadratic model with ground states corresponding to a full adder gate. |
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Generate a binary quadratic model with ground states corresponding to a half adder gate. |
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Generate a binary quadratic model with ground states corresponding to a multiplication circuit. |
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Generate a binary quadratic model with ground states corresponding to an OR gate. |
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Generate a binary quadratic model with ground states corresponding to an XOR gate. |
Optimization¶
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Generate a binary quadratic model encoding an independent set problem. |
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Generate a binary quadratic model encoding a maximum independent set problem. |
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Generate a binary quadratic model encoding a maximum-weight independent set problem. |
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Generate a bin packing problem as a constrained quadratic model. |
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Generates a constrained quadratic model encoding a knapsack problem. |
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Generate a constrained quadratic model encoding a multiple-knapsack problem. |
Random¶
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Generate a BQM for a doped ferromagnetic (FM) or antiferromagnetic (AFM) problem. |
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Generate a random binary quadratic model with a fixed number of variables and interactions. |
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Generate a BQM structured as an Erdős-Rényi graph. |
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Generate a binary quadratic model with random biases and offset. |
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Generate a random 2-in-4 satisfiability problem as a binary quadratic model. |
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Generate a bin packing problem as a constrained quadratic model. |
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Generates a constrained quadratic model encoding a knapsack problem. |
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Generate a constrained quadratic model encoding a multiple-knapsack problem. |
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Generate a random not-all-equal 3-satisfiability problem as a binary quadratic model. |
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Generate an Ising model for a RANr problem. |
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Generate a binary quadratic model with random biases and offset. |
Single-Variable Models¶
Generators for single-variable models used in symbolic math.
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Return a binary quadratic model with a single binary variable. |
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Yield binary quadratic models, each with a single binary variable. |
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Return a NumPy array of binary quadratic models, each with a single binary variable. |
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Return a quadratic model with a single integer variable. |
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Yield quadratic models, each with a single integer variable. |
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Return a NumPy array of quadratic models, each with a single integer variable. |
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Return a binary quadratic model with a single spin variable. |
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Yield binary quadratic models, each with a single spin variable. |
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Return a NumPy array of binary quadratic models, each with a single spin variable. |