Surrogate Modeling via Factorization Machine and Ising Model with Enhanced Higher-Order Interaction Learning

TL;DR

This paper introduces an enhanced surrogate modeling approach that integrates slack variables into factorization machines and Ising models, enabling higher-order interaction learning and improved prediction of drug combination effects, with potential quantum advantages.

Contribution

The paper presents a novel integrated surrogate model with slack variables for higher-order interactions, unifying the process and improving performance over previous methods.

Findings

Improved prediction accuracy for drug combination effects.

Effective incorporation of higher-order feature interactions.

Potential for leveraging quantum computing advantages.

Abstract

Recently, a surrogate model was proposed that employs a factorization machine to approximate the underlying input-output mapping of the original system, with quantum annealing used to optimize the resulting surrogate function. Inspired by this approach, we propose an enhanced surrogate model that incorporates additional slack variables into both the factorization machine and its associated Ising representation thereby unifying what was by design a two-step process into a single, integrated step. During the training phase, the slack variables are iteratively updated, enabling the model to account for higher-order feature interactions. We apply the proposed method to the task of predicting drug combination effects. Experimental results indicate that the introduction of slack variables leads to a notable improvement of performance. Our algorithm offers a promising approach for building efficient surrogate models that exploit potential quantum advantages.