Quantum-Based Optimization of Gas Throughput in Natural Gas Transmission Networks Under Hydraulic Constraints Using QAOA
Abstract
Maximizing gas throughput in transmission networks under hydraulic and operational constraints is a combinatorial problem whose complexity grows exponentially with network size, making it computationally intensive to solve exactly. This paper addresses the graph-based optimization problem by optimizing nodal-pressure assignments under the Panhandle-B hydraulic equation. By framing the problem as a search over discretized nodal-pressure assignments coupled with a cost Hamiltonian that encodes both the delivery objective and physical-constraint penalties, we establish a unified formulation suitable for the Quantum Approximate Optimization Algorithm (QAOA). The mathematical model is adapted to a Quadratic Unconstrained Binary Optimization (QUBO) formulation and implemented using the Classiq quantum software platform. In simulator-based experiments, QAOA recovered the maximum-throughput valid operating point, consistent with classical exhaustive evaluation and classical hydraulic simulation reference solutions. A distinctive contribution of this work is the end-to-end execution of a reduced problem instance on the IonQ Forte-1 trapped-ion quantum processor. Remarkably, the hardware implementation used only p=2 QAOA layers, substantially fewer than the p=30 layers used in the simulator-based study. Despite this significant reduction in circuit depth, the QPU produced physically valid and interpretable candidate solutions that bracketed the continuous classical optimum, with each located within one pressure-discretization step of it. These results demonstrate that meaningful gas-network optimization behavior can be obtained using considerably shallower QAOA circuits than initially expected and provide an end-to-end proof of concept for near-term quantum-assisted gas-network optimization.
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BibTeX
@misc{ishay2026quantumbased,
title = {{Quantum-Based Optimization of Gas Throughput in Natural Gas Transmission Networks Under Hydraulic Constraints Using QAOA}},
author = {Alex Ben Ishay and Yuval Eyal and Yuval Cohen and Nati Erez},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.00825},
eprint = {2609.00825},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.00825},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum-Based Optimization of Gas Throughput in Natural Gas Transmission Networks Under Hydraulic Constraints Using QAOA
AU - Alex Ben Ishay
AU - Yuval Eyal
AU - Yuval Cohen
AU - Nati Erez
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.00825
UR - https://arxiv.org/abs/2609.00825
AB - Maximizing gas throughput in transmission networks under hydraulic and operational constraints is a combinatorial problem whose complexity grows exponentially with network size, making it computationally intensive to solve exactly. This paper addresses the graph-based optimization problem by optimizing nodal-pressure assignments under the Panhandle-B hydraulic equation. By framing the problem as a search over discretized nodal-pressure assignments coupled with a cost Hamiltonian that encodes both the delivery objective and physical-constraint penalties, we establish a unified formulation suitable for the Quantum Approximate Optimization Algorithm (QAOA). The mathematical model is adapted to a Quadratic Unconstrained Binary Optimization (QUBO) formulation and implemented using the Classiq quantum software platform. In simulator-based experiments, QAOA recovered the maximum-throughput valid operating point, consistent with classical exhaustive evaluation and classical hydraulic simulation reference solutions. A distinctive contribution of this work is the end-to-end execution of a reduced problem instance on the IonQ Forte-1 trapped-ion quantum processor. Remarkably, the hardware implementation used only p=2 QAOA layers, substantially fewer than the p=30 layers used in the simulator-based study. Despite this significant reduction in circuit depth, the QPU produced physically valid and interpretable candidate solutions that bracketed the continuous classical optimum, with each located within one pressure-discretization step of it. These results demonstrate that meaningful gas-network optimization behavior can be obtained using considerably shallower QAOA circuits than initially expected and provide an end-to-end proof of concept for near-term quantum-assisted gas-network optimization.
ER -