On the Distortion of Partitioning Performance by Random Quantum Circuits
Abstract
Hypergraph partitioning is a central component of distributed quantum computing (DQC) compilers. However, due to the limited size of available quantum benchmark suites, many partitioning studies rely on random quantum circuits as evaluation workloads. In this work, we investigate whether such benchmarking practices provide a faithful assessment of partitioner performance. We evaluate a diverse set of state-of-the-art hypergraph partitioning strategies across three circuit origins: real algorithmic circuits, structured generated circuits, and fully random circuits. Our results show that random circuits significantly distort partitioning evaluation. They inflate cut costs, alter scaling trends across QPU counts and circuit sizes, and change the relative ranking of partitioning strategies. In contrast, structured generated circuits exhibit substantially lower distortion, more closely approximating real workload behaviour in cost, scaling, and strategy rankings. These findings demonstrate that benchmark selection directly influences methodological conclusions in DQC research and that random circuits may provide misleading guidance for compiler design.
Related
- Random quantum circuits, chaos and quantum thermalization
- Rare-History Transitions in Temporally Random Integrable Quantum Circuits
- One Gate at a Time: Complexity Growth in Random Quantum Circuits
- The quantum Mpemba effect in symmetric random Clifford circuits
- Complex Quantum Dynamics Versus Classical Simulability of Noisy Random Circuits
BibTeX
@misc{garces2026distortion,
title = {{On the Distortion of Partitioning Performance by Random Quantum Circuits}},
author = {Maria Gragera Garces},
howpublished = {arXiv},
year = {2026},
month = may,
doi = {10.48550/arxiv.2605.01974},
eprint = {2605.01974},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.01974},
}
RIS
TY - UNPB
TI - On the Distortion of Partitioning Performance by Random Quantum Circuits
AU - Maria Gragera Garces
PY - 2026
DA - 2026/05/11
PB - arXiv
DO - 10.48550/arxiv.2605.01974
UR - https://arxiv.org/abs/2605.01974
AB - Hypergraph partitioning is a central component of distributed quantum computing (DQC) compilers. However, due to the limited size of available quantum benchmark suites, many partitioning studies rely on random quantum circuits as evaluation workloads. In this work, we investigate whether such benchmarking practices provide a faithful assessment of partitioner performance. We evaluate a diverse set of state-of-the-art hypergraph partitioning strategies across three circuit origins: real algorithmic circuits, structured generated circuits, and fully random circuits. Our results show that random circuits significantly distort partitioning evaluation. They inflate cut costs, alter scaling trends across QPU counts and circuit sizes, and change the relative ranking of partitioning strategies. In contrast, structured generated circuits exhibit substantially lower distortion, more closely approximating real workload behaviour in cost, scaling, and strategy rankings. These findings demonstrate that benchmark selection directly influences methodological conclusions in DQC research and that random circuits may provide misleading guidance for compiler design.
ER -