Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value Theory

TL;DR

This paper introduces a method using Extreme Value Theory to estimate the number of quantum executions needed for effective feature selection in recommender systems, addressing hardware noise and probabilistic outcomes.

Contribution

It presents a novel EVT-based approach to quantify quantum resource requirements for feature selection, considering real-world hardware noise and variability.

Findings

Effectively estimates quantum runs needed for high-quality solutions

Validated on multiple quantum platforms and benchmark datasets

Addresses quantum hardware noise and probabilistic solution quality

Abstract

Recent advances in quantum computing have significantly accelerated research into quantum-assisted information retrieval and recommender systems, particularly in solving feature selection problems by formulating them as Quadratic Unconstrained Binary Optimization (QUBO) problems executable on quantum hardware. However, while existing work primarily focuses on effectiveness and efficiency, it often overlooks the probabilistic and noisy nature of real-world quantum hardware. In this paper, we propose a solution based on Extreme Value Theory (EVT) to quantitatively assess the usability of quantum solutions. Specifically, given a fixed problem size, the proposed method estimates the number of executions (shots) required on a quantum computer to reliably obtain a high-quality solution, which is comparable to or better than that of classical baselines on conventional computers. Experiments conducted across multiple quantum platforms (including two simulators and two physical quantum processors) demonstrate that our method effectively estimates the number of required runs to obtain satisfactory solutions on two widely used benchmark datasets.