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Embeding: k = 1, G(n, 0.25). Figure 6: Embedding: k = 1, G(n, 0.25).

Embeding: k = 1, G(n, 0.25). Figure 6: Embedding: k = 1, G(n, 0.25).

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Figure 1: k = 1, G(5, 0.25).
Figure 2: k = 1, G(5, 0.75).
Figure 3: k = 2, G(5, 0.25).
Figure 4: k = 2, G(5, 0.75).
+9 Figure 5: Embeding: k = 1, G(n, 0.25). Figure 6: Embedding: k = 1, G(n,...
Characterization of QUBO reformulations for the maximum k-colorable subgraph problem
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  • Jan 2021
Quantum devices can be used to solve constrained combinatorial optimization (COPT) problems thanks to the use of penalization methods to embed the COPT problem's constraints in its objective to obtain a quadratic unconstrained binary optimization (QUBO) reformulation of the COPT. However, the particular way in which this penalization is carried out...
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Context 1
... is true not only in terms of the average qubits required to embed each QUBO, but the volatility of the number of qubits required to embed each QUBO. In Figure 5, in which sparse graphs (i.e., p = 0.25) are used for the case k = 1, both QUBO formulations can be embedded, for graphs with up to n = 50, in D-Wave's 2000Q TM processor. However, in Figure 7, where dense graphs (i.e., p = 0.75) are considered, now the L-QUBO can be embedded only for graphs with up to n = 40. ...
Context 2
... is true not only in terms of the average qubits required to embed each QUBO, but the volatility of the number of qubits required to embed each QUBO. In Figure 5, in which sparse graphs (i.e., p = 0.25) are used for the case k = 1, both QUBO formulations can be embedded, for graphs with up to n = 50, in D-Wave's 2000Q TM processor. However, in Figure 7, where dense graphs (i.e., p = 0.75) are considered, now the L-QUBO can be embedded only for graphs with up to n = 40. ...

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+15 Embeding: k=1\documentclass[12pt]{minimal} \usepackage{amsmath}...
Characterization of QUBO reformulations for the maximum k-colorable subgraph problem
Article
Full-text available
  • Mar 2022
Quantum devices can be used to solve constrained combinatorial optimization (COPT) problems thanks to the use of penalization methods to embed the COPT problem’s constraints in its objective to obtain a quadratic unconstrained binary optimization (QUBO) reformulation of the COPT. However, the particular way in which this penalization is carried out...
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