Quantum for the Power Grid and Logistics: Promising Pilots, Early Evidence
Why logistics and energy are natural targets
Look at the problems: which power plants should run each hour, which truck takes which stop, which car goes down the paint line next. Every one has many options, hard rules and money on the line. Saving even a small percent matters, and that is why these industries keep appearing in quantum pitches. Our overview of general applications is in quantum computing use cases.
Energy: the unit commitment problem
Unit commitment means deciding which generators to switch on or off, and when, so that demand is met at the lowest cost while respecting each plant's limits. It is a hard mixed-integer problem that grid operators already solve with strong classical software.
On 31 July 2025, IonQ announced a demonstration with Oak Ridge National Laboratory and the US Department of Energy, under the GRID-Q project. As reported, the team used IonQ's 36-qubit Forte Enterprise machine in a hybrid workflow with classical computers on a problem with 26 generators and 24 time periods. ORNL described it as showing feasibility for this kind of device. The announcement says follow-up research is meant to test whether quantum advantage is possible as devices scale, so advantage is explicitly untested. It also includes a forward-looking statement: IonQ expects 100 to 200 high-fidelity qubits, possibly as early as 2026, to solve grid-scale unit commitment. That is a company projection, and the announcement does not provide peer-reviewed results or comparisons against classical solvers.
For context, the academic annealing benchmark discussed in our annealing guide found Gurobi beat D-Wave's hybrid solver on a unit commitment problem for both time and quality. So the bar is high, and fair testing matters.
Manufacturing: Ford Otosan and D-Wave
In March 2025, D-Wave and Ford Otosan announced a hybrid quantum application in production sequencing more than 1,500 Ford Transit variants through body shop, paint shop and assembly limits. The announcement reports scheduling for 1,000 vehicles per run dropping from 30 minutes to under five. Ford Otosan says the hybrid approach goes beyond what it achieved with a purely classical approach, and expects higher peak hourly throughput but gives no figure. This is a company-announced result, with no independent audit available in the sources I read. It is still notable because a factory is using it in production, which is more than a lab demo.
Routing: Volkswagen in Lisbon
In 2019 Volkswagen and the Lisbon transit operator Carris ran a pilot where a D-Wave computer calculated fastest routes for nine buses in near real time during a conference week, linking 26 stops into four routes. Volkswagen's release said passenger travel times would drop significantly, a forward-looking claim. I did not find a published measured evaluation. It is best viewed as an early proof of concept.
Simulated and quantum-inspired work
Some famous results ran on ordinary computers. In a 2019 simulation, Ford and Microsoft studied routing as many as 5,000 vehicles across Metro Seattle, with quantum-inspired methods on classical hardware. Ford reported a 73 percent improvement in total congestion compared with selfish routing and an 8 percent cut in average commute time, per Ford's account (outlets describe the baseline for the 8 percent figure differently). That is a simulation, and it shows the strength of quantum-inspired classical algorithms, which are a valid and useful outcome of quantum research.
The pattern across pilots
| Pilot | Reported | Still unproven |
|---|---|---|
| IonQ and ORNL grid | Feasibility on 36 qubits, 26 generators, 24 periods | Advantage over classical solvers |
| Ford Otosan | 30 minutes to under five for 1,000 vehicles, in production | Independent comparison against best classical |
| Volkswagen Lisbon | Near real time routing for nine buses | Measured travel time gains |
What would convince a skeptic?
- An open benchmark (see benchmarks explained) where quantum and best classical solvers face the same instances.
- Results reproduced by a team that does not sell the machine.
- Gains that grow with problem size, on hardware, not only in simulation.
- Total cost and time counted, including classical helpers.
Why to stay optimistic
Real companies are putting real problems on real machines, and the vendors are publishing numbers that can be tested. Even if the first wins are small or come from hybrid and quantum-inspired methods, they build skills and software that will be ready when fault-tolerant machines arrive. See the 2030 to 2035 outlook. This page is educational only and is not financial advice. Pilots by IonQ, D-Wave or others say nothing about the QNT memecoin, which is independent of all of them.
Sources and further reading
- IonQ: partnership with Oak Ridge on power grid optimization (July 2025)
- Ford Otosan and D-Wave announcement (March 2025)
- Volkswagen Group: Lisbon bus pilot (2019)
- arXiv 2409.05542: quantum annealing versus classical solvers
- Axios: Ford and Microsoft quantum-inspired traffic research (2019)
- Ford: exploring the quantum world with Microsoft to reduce congestion
Reported as of 2026-10-09. Pilot results are mostly announced by the companies involved. This is an educational overview, not financial advice. The QNT memecoin is independent of Quantinuum Ltd and of every bank, lab or company named here.
Frequently asked questions
Has quantum computing improved the power grid?
Not shown. IonQ and Oak Ridge reported a feasibility demo in July 2025 with 26 generators and 24 periods, and advantage is explicitly still to be tested.
Is Ford Otosan really using quantum in production?
Ford Otosan and D-Wave announced in March 2025 a hybrid quantum application in production for vehicle sequencing, reporting a cut from 30 minutes to under five. It is a company announcement.
Did the Volkswagen bus pilot work?
It ran with nine buses in Lisbon in 2019. The sources reviewed give forward-looking claims but no measured before-and-after results.
Is this financial advice?
No. It is an educational overview and not about any token or stock.
Keep reading
- Annealing vs Gate Model for Optimization: Which Quantum Approach Helps Real Businesses?
D-Wave style quantum annealers and gate-model machines from IBM, IonQ and Quantinuum aim at optimization differently. Here is what each does, what the benchmarks say, and how to stay fair. - Quantum Computing Use Cases: What Could It Actually Do?
From drug discovery to logistics and cryptography, here are the realistic use cases of quantum computing. - Where Quantum Finance Claims Outrun the Evidence: A Hype Checklist
A friendly checklist for reading quantum finance, logistics and optimization headlines: baselines, simulation versus hardware, up-to numbers, and why quantum news is not token news. - Banks and Quantum Computing: What JPMorgan, HSBC and Others Have Actually Shown
A plain English tour of the real bank pilots with quantum computers, from JPMorgan's certified randomness to HSBC's bond trading trial, and what each one does and does not prove.
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