Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
For researchers in quantum machine learning, this work identifies a fundamental security vulnerability in distributed quantum algorithms that can undermine their reliability.
The paper reveals that adversarial perturbations of shared entanglement in distributed variational quantum algorithms introduce structured gate-level noise, which can bias optimization toward incorrect solutions while avoiding barren plateaus. Numerical simulations validate the adversarial degradation of expressibility and trainability.
Distributed quantum algorithms offer a promising pathway to scale variational quantum algorithms beyond the constraints of noisy intermediate-scale quantum hardware. However, existing approaches implicitly assume a trusted entanglement-sharing layer across quantum processors. We show that this assumption introduces a fundamental vulnerability: adversarial perturbations of shared entanglement induce structured gate-level noise that directly impacts quantum learning. We develop a framework that maps entanglement-level perturbations to gate-level noise via an explicit Kraus representation. To quantify their impact, we introduce Kraus expressibility, a metric that generalizes unitary expressibility to noisy quantum channels. We then establish a trade-off between Kraus expressibility and trainability of noisy quantum circuits through gradient variance analysis. Our analysis reveals that an adversary can manipulate Kraus expressibility to maintain sufficiently large cost gradients (avoiding barren plateaus) while systematically biasing optimization toward incorrect solutions. We validate these findings through numerical simulations, demonstrating adversarial degradation of expressibility and trainability.