Insider Brief
- Classiq and Israel Natural Gas Lines (INGL) researched quantum optimization for natural gas transmission planning, including execution of a reduced problem on IonQ ’s Forte-1 quantum processor.
- The hybrid quantum-classical workflow searched pressure configurations while accounting for supplier pressure, pipe characteristics, minimum customer pressure and other network constraints.
- Simulator testing identified a maximum-throughput valid operating point consistent with classical reference solutions, while Forte-1 produced physically valid candidate points close to the classical optimum.
PRESS RELEASE — Classiq and Israel Natural Gas Lines Ltd. (INGL) announced results from research applying quantum optimization to natural gas transmission planning, including execution of a reduced version of the problem on the IonQ Forte-1 quantum computer.
The collaboration focused on a familiar challenge for pipeline operators: moving gas from suppliers to customers while staying within limits for pressure, flow direction and gas balance across the network.
As networks grow and operating conditions become more complex, the number of possible pressure and flow configurations can increase rapidly. The research explored whether quantum optimization could help identify promising scenarios more systematically, giving engineers a stronger starting point for detailed review.
For gas transmission operators, the current goal is not to replace established hydraulic modeling but to improve how candidate plans are generated and evaluated. Quantum optimization could help narrow a large set of possibilities to a smaller number of promising options for engineering validation.
Using Classiq ’s platform, Classiq and INGL created a hybrid quantum-classical workflow that searched possible pressure configurations while accounting for key physical and operational requirements. The work used familiar network inputs, including supplier pressure, pipe characteristics and minimum customer pressure.
In simulator-based testing on a representative gas network, the workflow identified the maximum-throughput valid operating point, consistent with classical reference solutions. The research then moved a version of the problem onto the IonQ Forte-1 trapped-ion quantum processor, where the system produced physically valid candidate operating points close to the classical optimum.
The collaboration also examined how candidate scenarios could be reviewed with SIMONE, a detailed hydraulic modeling environment for gas networks. In this approach, quantum optimization helps identify promising planning options, while established hydraulic modeling can be used to validate them before any operational conclusion is drawn.
Technical Deep Dive
The full research paper provides the mathematical formulation, quantum optimization methodology, simulator results and details of the IonQ Forte-1 hardware execution.
Read the full paper on arXiv: https://arxiv.org/pdf/2609.00825
“Pipeline planning depends on practical engineering constraints and not abstract optimization alone,” said Nir Minerbi , co-founder and CEO of Classiq . “Our work with INGL focused on bringing quantum optimization into a language oil and gas teams recognize: pressure, flow, constraints and validation. The goal is to surface strong candidate scenarios for further engineering review complementing trusted tools operators already use.”
“INGL has a responsibility to combine operational discipline with forward-looking innovation. Our work with Classiq reflects that strategy,” said Shlomo Kresner, CEO at INGL. “We are exploring quantum optimization not as a theoretical exercise, but as an emerging tool for evaluating complex network scenarios, validating them against established engineering methods and building practical knowledge for medium-term implementation.”
“This research shows the value of testing industry applications on real quantum hardware as they are developed,” said Scott Millard, Chief Business Officer at IonQ . “Running the gas-network optimization problem on Forte-1 gave the team a practical way to evaluate how quantum computing could contribute to complex planning challenges in the energy sector.”
The work reflects a practical approach to quantum computing for oil and gas and the wider energy industry. Energy operators regularly balance capacity, customer demand, infrastructure constraints and changing operating conditions. The research explores how quantum optimization could eventually complement the planning and simulation tools they already use.