On Quadratization of Pseudo-Boolean Functions

Abstract

We survey current term-wise techniques for quadratizing high-degree pseudo-Boolean functions and introduce a new one, which allows multiple splits of terms. We also introduce the first aggregative approach, which splits a collection of terms based on their common parts.

11 pages

References in corpus (1)

• The Expressive Power of Binary Submodular Functions

Cited by in corpus (4)

• Bayesian Network Structure Learning Using Quantum Annealing
• Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing
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• Quadratization of Symmetric Pseudo-Boolean Functions