Benchmarking Zero-Setup Quantum Circuit Simulators
Abstract
Practitioners increasingly rely on hosted simulation environments, but their performance characteristics remain poorly documented. We present a systematic benchmarking study of GPU-accelerated approximate quantum simulation across two widely used methods: matrix product states (MPS) and Pauli path simulation (PPS), comparing BlueQubit (a hosted tool that handles hardware provisioning, simulator configuration, and job orchestration) against AWS Braket, Quantum Rings, Qiskit pauli-prop, and PauliPropagation (written in Julia). For MPS, we find that GPU runtime yields sub-quadratic scaling with bond dimension, with a growing advantage over CPU at increasing scale. For Pauli path simulation on IBM's 127-qubit kicked Ising benchmark, GPUs deliver up to ∼1,700× speedup at fine truncation thresholds (δ= 2.5 × 10−5, 27.6M Pauli terms), and are the only backends that reach accuracy regimes below δ= 10−5, which remained inaccessible to the commodity CPU-based implementations and self-contained SDKs evaluated here. We also provide a reproducible characterization of these simulators across regimes, including tradeoffs that isolated evaluations do not show. All benchmarking code and configurations are in a public GitHub repository.
Related
- Benchmarking Optimizers to Solve Inverse Problems with Differentiable Physics Simulators
- Adversarial Robustness in Fake Quantum Simulators
- Residual-Guided Global-Drive Design in Trapped-Ion Quantum Simulators
- Characterizing quasiparticles in strongly correlated systems using nonlinear spectroscopy in quantum simulators
- Quickly extracting fidelity decay rates in random circuit benchmarking experiments
BibTeX
@misc{mazumder2026benchmarking,
title = {{Benchmarking Zero-Setup Quantum Circuit Simulators}},
author = {Arul Rhik Mazumder and Hovnatan Karapetyan and Mohammed Zuhair Mullath and Rudy H. Tanin and Hrant Gharibyan and Hayk Tepanyan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.09882},
eprint = {2607.09882},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.09882},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Benchmarking Zero-Setup Quantum Circuit Simulators
AU - Arul Rhik Mazumder
AU - Hovnatan Karapetyan
AU - Mohammed Zuhair Mullath
AU - Rudy H. Tanin
AU - Hrant Gharibyan
AU - Hayk Tepanyan
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2607.09882
UR - https://arxiv.org/abs/2607.09882
AB - Practitioners increasingly rely on hosted simulation environments, but their performance characteristics remain poorly documented. We present a systematic benchmarking study of GPU-accelerated approximate quantum simulation across two widely used methods: matrix product states (MPS) and Pauli path simulation (PPS), comparing BlueQubit (a hosted tool that handles hardware provisioning, simulator configuration, and job orchestration) against AWS Braket, Quantum Rings, Qiskit pauli-prop, and PauliPropagation (written in Julia). For MPS, we find that GPU runtime yields sub-quadratic scaling with bond dimension, with a growing advantage over CPU at increasing scale. For Pauli path simulation on IBM's 127-qubit kicked Ising benchmark, GPUs deliver up to ∼1,700× speedup at fine truncation thresholds (δ= 2.5 × 10^−5, 27.6M Pauli terms), and are the only backends that reach accuracy regimes below δ= 10^−5, which remained inaccessible to the commodity CPU-based implementations and self-contained SDKs evaluated here. We also provide a reproducible characterization of these simulators across regimes, including tradeoffs that isolated evaluations do not show. All benchmarking code and configurations are in a public GitHub repository.
ER -