D-Wave Brings Quantum Optimization to Startups, Manufacturers, and Retailers
Manufacturers, grocers, and agri-tech startups are already using quantum computing to solve real-world scheduling and logistics challenges
Quantum computing has long been seen as the domain of tech giants, deep-pocketed defense contractors, and elite research labs. But for D-Wave, that perception is not just outdated—it’s counterproductive. In 2025, its quantum systems are powering optimization solutions for a surprising range of businesses, including regional grocery chains, agricultural startups, and auto factories.
“We’re not talking about 2030,” said Lorenzo Martinelli, Chief Revenue Officer at D-Wave. “There are customers of ours in production and doing workforce schedules right now.”
For D-Wave, the key application is optimization—using algorithms to find the most efficient outcome for problems with many variables and constraints. It’s a foundational need in sectors like manufacturing, logistics, and retail workforce planning, and one where classical computing has started to hit its limits.
“How do you formulate mathematically something that goes on in the real world, and try to figure out the minimum and maximum? You're trying to optimize time, resources, investments, or anything like that,” Martinelli said.
Use Cases Across Industries
Speaking at Commercializing Quantum Global 2025 in London on May 14, Martinelli described how the company is delivering results for customers already using quantum systems in live operations.
He pointed to a Canadian grocery chain with over 200 stores that uses quantum-powered optimization for workforce scheduling. Implemented initially for home delivery, the system now helps store managers react quickly to last-minute staffing changes, improving fairness and speed. What used to take 30 to 45 minutes now takes less than five.
When a worker calls in sick or a shift needs to be changed, Martinelli explained, managers often revert to calling the same reliable staff. That pattern discouraged other employees and caused operational strain. With a more data-driven system, scheduling decisions are spread more evenly. The solution has been running in production for three years and is now up for renewal.
Martinelli said the size of a business is not a limiting factor.
“There are small companies that do this. You don’t have to be a big company to do that,” he said.
In agriculture, a Canadian startup is using D-Wave’s system to guide autonomous harvesting vehicles across large fields. The problem is how to optimize the machines’ path to maximize efficiency and yield.
“It’s fascinating,” Martinelli said, “as long as there’s a mathematical optimization problem, quantum gives you a power that just wasn’t available before—and it’s available today.”
In Turkey, Ford Otosan is using D-Wave to improve its commercial vehicle production line. Since every van is built to custom specifications, sequencing their assembly is a difficult puzzle. D-Wave’s system compresses that scheduling time from more than 30 minutes to under five, while reducing fluctuations in labor and component demands. Martinelli said this has helped Ford improve process flow and smooth out production peaks.
No Hardware Investment Needed
Despite its reputation for complexity, quantum computing is increasingly accessible. Most customers do not need to purchase or maintain their own quantum hardware.
According to Martinelli, users access D-Wave’s platform through the cloud in the same way they’d submit a job to any modern software-as-a-service system. The cost of a proof-of-concept deployment typically falls in the low to mid-six-figure range.
What businesses need is someone who can mathematically define the problem they want to solve.
“If you don’t have one, we have professional services. We have partners that can help you formulate the problem and then take your data, and let’s apply and see: can we provide you a better answer faster?” he said.
Martinelli emphasized that quantum computing should not be used for every problem.
“If it’s a problem that you can do with classical computing, don’t even bother doing quantum,” he said. “Go after the harder problems.”
Improving Networks, Not Just Speed
Martinelli also shared a case study from NTT Docomo in Japan. The mobile operator wanted to reduce the overhead caused by its network constantly “pinging” user devices to locate them in the mobile grid. While this pinging is essential for service delivery, it consumes a significant amount of bandwidth.
The company had used classical computing to optimize this process, but could only handle about 130 variables, far below the scale of the real-world problem. D-Wave’s quantum system increased that to over 2 million variables.
While Martinelli noted that the classical process took 26 hours and quantum brought it down to 40 seconds, he clarified that speed was not the main benefit.
“The speed doesn’t matter to them, because they run this thing every six months,” he said. “What matters is they can now free up 15% more network capacity.”
That freed-up capacity can either serve more customer transactions—texts, calls, data—or reduce the need to invest in as many physical towers.
“They have 250,000 of those across Japan. They cost between $20,000 and $200,000 each. You can do the math,” he said.
At a recent event hosted by SAS, a US-based provider of AI and data analytics platforms, a global consumer goods company described how it used D-Wave’s quantum solution to optimize shampoo production.
“You have all these different variations,” Martinelli said. “You’ve got tanks, you’ve got ingredients. How do you optimize that? Very complex problem—10 to the 124th power in terms of the combination possible.”
On classical systems, the job took six hours. With quantum, it now runs in under 12 minutes. The improvement has allowed the manufacturer to adapt production schedules much more rapidly in response to inventory and demand changes.
Toward Enterprise Readiness
D-Wave distinguishes itself from academic or experimental platforms by offering a commercial-grade solution that is already supporting mission-critical processes.
“We are the only company that has a service-level agreement (SLA) for quantum computing today,” Martinelli said. “Your systems are at queue time in sub-seconds, not hours. We have all the information security and things to solve a problem today.”
Martinelli, who spent more than two decades in optimization before joining D-Wave, said he was initially unaware of how mature the company’s platform had become.
“We’ve had customers who are coming out from the three-year renewal of production capability,” he said.
While other vendors chase long-term breakthroughs in gate-model quantum computing, D-Wave is staking its position in the here and now. Its annealing-based platform is already live in factories, stores, and telecom systems—not as research pilots, but as production-grade tools.
This approach gives D-Wave a first-mover advantage in demonstrating how quantum computing can solve business-critical problems without perfect qubits or futuristic hardware. Its success hinges on delivering outcomes where traditional infrastructure struggles: high-variability complexity, short decision windows, and shifting operational constraints.