Efficient Quantum Algorithm for Robust Training

TL;DR

This paper introduces a quantum algorithm for robust adversarial training in machine learning, significantly reducing computational costs by reformulating the problem as a sparse linear system.

Contribution

It presents an end-to-end quantum procedure for robust training that scales efficiently with training steps and model size, advancing AI security methods.

Findings

Query cost scales linearly with training steps

Polylogarithmic scaling with model size

Quantum approach reduces training overhead for robustness

Abstract

Adversarial training is a standard defense against malicious input perturbations in security-critical machine-learning systems. Its main burden is structural: before every parameter update, the current model must first be attacked to find a new adversarial perturbation, making training increasingly expensive and hard to sustain at large-model scale. Here we give an end-to-end quantum procedure for projected-gradient robust training under local stability and sparsity assumptions. The key step is to reformulate the coupled attacker--learner dynamics as a high-dimensional sparse linear system whose terminal block yields the final network-parameter state. In this formulation, the dominant query cost scales linearly with training time steps, up to logarithmic factors, and polylogarithmically with model size, while the full gate complexity records separate input-preparation and sparse-access overheads. This places core computational tasks for AI security on a concrete quantum footing and identifies a regime in which robust-training overhead can be reduced.