Quantum computing is no longer a distant promise; it's becoming a practical tool for solving expensive business problems today. A new survey of over 1,000 senior decision-makers at large UK firms found that 65% are already adopting quantum computing or running pilot projects, with 41% expecting the technology to unlock more than £100 million in value within a single year of deployment. This marks a dramatic shift from the technology's reputation as perpetually "five years away."

What Business Problems Is Quantum Computing Actually Solving?

The early adopters aren't chasing abstract physics breakthroughs. Instead, they're targeting a specific category of real-world headaches: combinatorial optimization, the mathematical puzzles that classical computers struggle with as problems grow larger and more complex. These are the daily operational challenges that eat into profit margins and tie up resources.

The most promising near-term applications cluster around logistics and manufacturing. Survey respondents rated the following as ripe for quantum-powered improvement:

• Workforce Scheduling: 90% of leaders flagged this as a priority, where quantum can optimize thousands of shift assignments around competing constraints.
• Resource Allocation: 89% identified this as a key opportunity, from equipment deployment to inventory positioning across supply networks.
• Supply Chain Optimization: 88% see potential in routing, vendor selection, and logistics coordination that currently require expensive manual tuning.
• Manufacturing Processes: 82% believe quantum can improve production sequencing and throughput planning.

These aren't theoretical applications. In 2019, Volkswagen ran a pilot using a D-Wave quantum annealer to optimize bus routes for thousands of attendees at the Web Summit in Lisbon, trimming total routing distance by around 6%. While modest, that result demonstrated the technology could deliver measurable business value on a real problem.

Why Are Companies Moving Faster Than Expected?

The survey reveals a telling pattern: organizations already using quantum computing estimate its commercial value at nearly twice the level of those still waiting on the sidelines. Among active users, 37% reported the technology is delivering value today, compared to just 16% across the broader sample. This suggests hands-on experience, not marketing hype, is driving adoption.

The momentum extends beyond optimization alone. Some 87% of respondents believed quantum computing could help optimize AI-related processes and other compute-heavy workloads, whether by sharpening optimization problems buried inside machine-learning pipelines or by handling specific tasks more efficiently. This pairing of quantum and artificial intelligence reflects how buyers are thinking about the technology: not as a replacement for classical computing or AI, but as a complementary tool.

How Are Hardware Makers Scaling Quantum Systems?

On the hardware side, companies are making tangible progress toward more powerful systems. Rigetti Computing made its 108-qubit Cepheus-1-108Q system generally available in the first quarter of 2026 through multiple cloud platforms including Amazon Braket, Microsoft Azure Quantum, and qBraid. The system is built using 12 interconnected 9-qubit chiplets, validating Rigetti's modular scaling architecture as a path toward larger machines.

Rigetti's revenue nearly tripled year-over-year to $4.4 million in the first quarter of 2026, driven primarily by deliveries of quantum processing units and related contracts to universities, research institutions, national laboratories, and commercial customers in materials science, logistics, and financial services. The company targets approximately 99.5% median two-qubit gate fidelity, a measure of computational accuracy, later in 2026 while maintaining speed advantages.

Management believes Rigetti can achieve quantum advantage, a milestone where quantum systems outperform classical computers on practical problems, within roughly three years by targeting a system with around 1,000 qubits, 99.9% two-qubit gate fidelity, and integrated error mitigation capabilities. Backed by approximately $569 million in cash and no debt, the company is well positioned to invest aggressively in scaling.

Meanwhile, D-Wave Quantum announced it has entered into a non-binding Letter of Intent with the U.S. Department of Commerce for proposed funding of up to $100 million under the CHIPS and Science Act. The initiative aims to support the advancement and large-scale deployment of D-Wave's annealing and gate-model quantum computing technologies. As part of the arrangement, the U.S. government would receive a $100 million equity stake in D-Wave. The funding is expected to help expand quantum system development at D-Wave's planned facility in Boca Raton, Florida, as well as its existing research and development operations.

D-Wave laid out an ambitious roadmap at its first Investor Day, targeting 100 logical qubits able to run more than a million operations by 2032. The plan stages the climb deliberately, starting with a 17-physical-qubit system in 2026, a 49-qubit machine in 2027, a 181-qubit device in 2028, a 10-logical-qubit system in 2030, then the 2032 target. The company's "dual-rail" qubit design builds error detection into the hardware itself, flagging roughly 90% of errors as they occur, which in principle reduces the number of physical qubits needed to protect each logical one.

"The era of enterprise quantum computing adoption has arrived. Companies are no longer asking if they should explore quantum, but how quickly they can implement it," said Murray Thom, vice president of quantum technology evangelism at D-Wave.

Murray Thom, Vice President of Quantum Technology Evangelism at D-Wave Quantum

IonQ also expanded its quantum footprint by opening a new 22,000-square-foot research and development laboratory and semiconductor chip testing facility in Boulder, Colorado, to support the development of future generations of its trapped-ion quantum computing systems. The facility will enable the company to design, test, and refine advanced semiconductor ion-trap chips, with plans to install its first quantum computer later in 2026.

What Should Organizations Do to Prepare for Quantum?

For companies considering quantum computing, the survey data suggests a practical path forward:

• Start with Optimization Problems: Identify internal challenges in scheduling, routing, resource allocation, or manufacturing that consume significant time or cost. These are the problems quantum systems are built to solve today, not theoretical applications years away.
• Run Pilot Projects: The survey shows that hands-on experience drives both confidence and measurable value. Organizations already using quantum estimate its worth at nearly double the level of those waiting, suggesting pilots unlock insights that surveys alone cannot capture.
• Plan for Quantum-AI Integration: As 87% of respondents expect quantum to optimize AI workloads, begin thinking about how quantum and AI tools might complement each other in your infrastructure, particularly for energy-intensive machine-learning tasks.
• Monitor Hardware Milestones: Track announcements from Rigetti, D-Wave, IonQ, and other vendors. The roadmaps being published now, with specific qubit counts and timelines, will determine which systems become available for your use cases in the next two to three years.

The quantum computing landscape is shifting from "when will this matter?" to "how do we implement this?" The survey of UK enterprises, combined with hardware advances from Rigetti, D-Wave, and IonQ, suggests the technology is moving from the lab to the logistics desk faster than many expected. For organizations in logistics, manufacturing, and supply chain management, the question is no longer whether quantum computing is worth exploring, but how quickly they can pilot it on their most expensive optimization problems.