Adversarial Robustness of Partitioned Quantum Classifiers

Abstract

Adversarial robustness in quantum classifiers is a critical area of study, providing insights into their performance compared to classical models and uncovering potential advantages inherent to quantum machine learning. In the NISQ era of quantum computing, circuit cutting is a notable technique for simulating circuits that exceed the qubit limitations of current devices, enabling the distribution of a quantum circuit's execution across multiple quantum processing units through classical communication. We examine how partitioning quantum classifiers through circuit cutting increase their susceptibility to adversarial attacks, establishing a link between attacking the state preparation channels in wire cutting and implementing adversarial gates within intermediate layers of a quantum classifier. We then proceed to study the latter problem from both a theoretical and experimental perspective.