@article{VALLERO2026107927,
title = {State of practice: Evaluating GPU performance of state vector and tensor network methods},
journal = {Future Generation Computer Systems},
volume = {174},
pages = {107927},
year = {2026},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2025.107927},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X25002225},
author = {Marzio Vallero and Paolo Rech and Flavio Vella},
keywords = {Quantum, Tensor network, HPC},
abstract = {The frontier of quantum computing (QC) simulation on classical hardware is quickly reaching the hard scalability limits for computational feasibility. Nonetheless, there is still a need to simulate large quantum systems classically, as the Noisy Intermediate Scale Quantum (NISQ) devices are yet to be considered fault tolerant and performant enough in terms of operations per second. Each of the two main exact simulation techniques, state vector and tensor network simulators, boasts specific limitations. This article investigates the limits of current state-of-the-art simulation techniques on a test bench made of eight widely used quantum subroutines, each in different configurations, with a special emphasis on performance. We perform both single process and distributed scaleability experiments on a supercomputer. We correlate the performance measures from such experiments with the metrics that characterise the benchmark circuits, identifying the main reasons behind the observed performance trends. Specifically, we perform distributed sliced tensor contractions, and we analyse the impact of pathfinding quality on contraction time, correlating both results with topological circuit characteristics. From our observations, given the structure of a quantum circuit and the number of qubits, we highlight how to select the best simulation strategy, demonstrating how preventive circuit analysis can guide and improve simulation performance by more than an order of magnitude.}
}
