What happens when a quantum computer is asked to figure out how to move natural gas through a pipeline network as efficiently as possible?
That is the question explored by Israeli quantum computing company Classiq and Israel Natural Gas Lines (INGL), which have tested a quantum optimization approach for natural gas transmission planning, including running a reduced version of the problem on IonQ’s Forte-1 quantum computer.
The challenge is a practical one. Gas network operators need to balance supply and customer demand while staying within constraints involving pressure, flow direction and gas balance. As networks become larger and operating conditions more complicated, the number of possible configurations can grow rapidly.
The research explored whether quantum optimization could help sift through those possibilities and identify promising operating scenarios for engineers to examine.
Using Classiq’s platform, the teams developed a hybrid quantum-classical workflow that searched for pressure configurations while taking into account factors including supplier pressure, pipeline characteristics and minimum pressure requirements for customers.
In simulations involving a representative gas network, the system identified a valid configuration that maximized throughput and matched classical reference solutions.
The researchers then took the experiment a step further, running a reduced version of the optimization problem on IonQ’s Forte-1 trapped-ion quantum processor. The quantum hardware produced physically valid candidate operating points that were close to the classical optimum.
That does not mean quantum computers are ready to run gas networks. The researchers emphasize that the technology is intended to complement, rather than replace, established hydraulic modeling.
Candidate solutions generated through quantum optimization can still be checked using conventional engineering tools. The research also examined this process using SIMONE, a hydraulic modeling environment used for gas networks.
The significance of the experiment is therefore less about replacing existing infrastructure and more about testing where quantum computing might fit into complex industrial planning.
For energy companies, optimization problems can involve huge numbers of possible combinations: capacity, demand, infrastructure limitations and changing operating conditions all have to be considered simultaneously.
The Classiq and INGL research suggests that quantum optimization could eventually become another tool for narrowing those possibilities down.