Two million logic gates. That is about what a quantum computer needed to model how fluid creeps through the gap between two parallel plates, a classic physics problem called Hele-Shaw flow. Researchers at Oak Ridge National Laboratory (ORNL) report their new LuGo quantum algorithm cuts that to 91,000 gates. That is a drop of more than 95 percent.
The target is a useful kind of problem: how liquids and gases move through tight spaces. Think groundwater near a contamination plume, or blood in a microfluidic lab-on-a-chip. ORNL describes those uses as future possibilities, not results.
Before and after LuGo
| Item | Before LuGo | With LuGo | Source |
|---|---|---|---|
| Gate count, Hele-Shaw flow | About 2,000,000 | 91,000 | Quantum Insider, Quantum Computing Report |
| Reduction | Not applicable | More than 95% | Quantum Insider |
| QPE share of effort | More than 90% (earlier work) | Not reported | Quantum Insider |
| Classical prep | Not applicable | 1 Frontier node of nearly 10,000 | Quantum Insider |
The old bottleneck
A popular quantum method for linear equations, the Harrow-Hassidim-Lloyd (HHL) algorithm, needs a setup step called quantum phase estimation, or QPE. In the team’s earlier Hele-Shaw work, QPE used more than 90 percent of the computing effort. ORNL computational scientist Murali Gopalakrishnan Meena said the team “needed 2 million gates to perform the necessary calculations.”
Each gate is a basic operation. Today’s quantum chips are noisy, and noise piles up with every gate. A circuit with millions of gates is out of reach for current hardware.
What LuGo changes
Chao Lu’s team built an improved QPE method called LuGo. It was detailed in reporting from The Quantum Insider, which reprinted an ORNL release. LuGo moves heavy preparation to classical computers before the problem reaches a quantum chip. That prep needed just one node of ORNL’s Frontier supercomputer, out of nearly 10,000. The paper appeared in Future Generation Computer Systems.
The team then evaluated the circuit on quantum hardware reached through a U.S. Department of Energy program: Quantinuum’s H-1 trapped-ion system, IBM’s Marrakesh and Sherbrooke chips, and IQM’s Garnet and Sirius chips. That is five processors from three companies.
“With LuGo, we reduced the computational effort tremendously and observed better overall performance,” Lu said.
Kalyan Gottiparthi, another ORNL co-author, said the team expects “we’ll find more of these kinds of approaches.” LuGo also won a 2026 R&D 100 Award.
Why It Matters to You
Nothing here is available to you yet, and it may never reach you directly. It is research, and ORNL’s authors note that practical gains depend on fault-tolerant quantum computers that do not exist at scale. Picture a civil engineer we’ll call Marcus, an illustrative example, not a real person, who runs a small water utility downstream from a closed factory and needs groundwater models to check on wells. Smaller quantum circuits might one day let simulations like his run on less hardware. That is a hope, not a result.
We saw a similar pattern in a quantum method that SQC reports improved energy forecasts by up to 41 percent. Smaller steps add up, but each one is still far from everyday use.
Where it falls short
- The Quantum Insider report says the validation relied on classical simulation. It lists the quantum processors but reports no hardware accuracy results.
- The Quantum Computing Report says the release gives no solution accuracy, fidelity, or error rates from the hardware runs, so it can’t show whether the smaller circuits give usable answers on noisy devices.
- The 95 percent figure is described inconsistently. The Quantum Computing Report’s text calls it total gate operations, while its table calls it circuit depth.
- The gain relies on a large classical preprocessing step. No source we found weighs that cost against the quantum savings.
- No source reports qubit counts, grid size, or problem dimensions, so we can’t say how big a flow problem LuGo handled.
- In the team’s earlier study, ORNL said it did not demonstrate quantum advantage. Meena said the advantage is theoretical, and noise models did not match real hardware behavior.
Quick answers
What is LuGo?
It is a quantum phase estimation method from ORNL. It does more of the work on classical computers first, so the quantum circuit needs far fewer gates.
Did LuGo run on real quantum computers?
ORNL used hardware from Quantinuum, IBM, and IQM through a DOE program. The Quantum Insider report says validation relied on classical simulation and lists no hardware accuracy results.
Does this show quantum advantage for fluid flow?
No. ORNL’s earlier Hele-Shaw study said quantum advantage was theoretical and not the goal. LuGo shrinks a circuit. It does not prove a speedup over classical solvers.
When could this help real water or medical models?
The authors say practical gains need fault-tolerant machines that do not exist at scale yet. Uses like groundwater and microfluidics are listed as possible future applications.
Sources
- The Quantum Insider: New Quantum Algorithm Targets Bottleneck in Fluid Flow Modeling (Sep 30, 2026)
- Quantum Computing Report: ORNL Develops LuGo Algorithm on Frontier Supercomputer (Oct 02, 2026)
- Quantum Zeitgeist: ORNL Demonstration, Quantum Algorithm Tackles Fluid Dynamics (Sep 30, 2026)
- Oak Ridge National Laboratory: Quantum Study Feels Out Questions in Fluid Flow (Mar 25, 2025)