Toward Secure Multitenant Quantum Computing: Circuit Affinity, Crosstalk Patterns, and Grouping Strategies
Abstract
Multitenancy increases throughput and reduces costs in cloud-based quantum computing, but concurrent job execution introduces security risks through inter-circuit crosstalk. We characterize the structural predictability of these interference patterns across seven IBM superconducting processors, spanning Heron (r1-r3) and Nighthawk (r1) architectures and five different circuit types. We evaluate pairwise interactions, by applying the Structural Similarity Index (SSIM) and a structural -statistic to the concurrent execution of five foundational quantum circuits (QAOA, Grover's, QPE, QFT, and ZZFeatureMap), we quantify behavioral consistency across disparate hardware. Our results identify three types of circuits: universally aggressive, universally sensitive, and cotenant-dependent circuits. Aggressive circuits, such as Grover's Algorithm, exhibit a statistically significant interference pattern, yielding a -statistic range of relative to the standalone baselines across all tested pairings. Conversely, sensitive circuits, such as the Quantum Fourier Transform, demonstrate a disproportionate susceptibility to multitenant execution, showing high deviations from single-tenant computational behavior. We demonstrate that crosstalk signatures are highly consistent within architectural revisions--with intra-revision similarity reaching (Hr3) and (Hr2)--while inter-revision similarity drops to . Furthermore, we identify a ``topological decoupling" between Heavy-Hex and square lattice systems, where structural similarity falls to between Heron r1 and Nighthawk r1. These findings provide an empirical foundation for hardware-aware schedulers to strategically pair jobs, maximizing system utilization while preserving computational integrity.
Cite
@article{arxiv.2605.00118,
title = {Toward Secure Multitenant Quantum Computing: Circuit Affinity, Crosstalk Patterns, and Grouping Strategies},
author = {Andrew Woods and Chi-Ren Shyu},
journal= {arXiv preprint arXiv:2605.00118},
year = {2026}
}
Comments
11 Pages, 8 Figures. This work was submitted to an IEEE conference for possible publication