Quantum computing has passed through its first major milestone, including demonstrations of supremacy, where a quantum system can perform a task beyond the reach of classical computers. But supremacy, while important, is different from usefulness. As researchers, engineers, and policymakers shift their focus from performance demonstrations to practical applications, a new benchmark is emerging. Quantum systems must justify their cost, energy consumption, and complexity by solving problems that truly matter to society. Erik Hosler, a specialist in advanced lithography who participated in the SPIE panel, emphasized the importance of utility over novelty, highlighting the role of real-world value in shaping next-generation architectures.
To reach this level of impact, quantum computers must not only work but must outperform classical systems. That means delivering results that would be economically infeasible, physically impossible, or prohibitively slow on classical systems. In this next phase, success will not be judged by speed or scale alone. It will hinge on whether quantum systems can unlock new insights, deliver tangible outcomes, and justify the immense effort that goes into building and operating them.
From Demonstration to Deployment
The path to quantum usefulness begins by identifying which problems benefit most from quantum approaches. Current quantum supremacy demonstrations have largely focused on contrived benchmarks, tasks designed to exploit quantum parallelism but with little real-world value. These experiments are essential to validating the model, but they offer limited insights into applied utility.
Now the focus is turning to domains like molecular simulation, logistics optimization, machine learning acceleration, and cryptography. These problems have real stakes in fields like pharmaceuticals, materials design, national security, and climate modeling. The aim is to solve practical problems faster, more accurately, or more efficiently than any classical approach could manage.
The bar for usefulness is higher than for supremacy. It requires error correction, longer coherence times, high qubit connectivity, and reliable software stacks. It also demands that hardware be designed with application needs in mind, rather than just architectural experimentation. “It must impact society at large. The value of the computations it performs exceeds the cost to build and operate the computer,” Erik Hosler notes.
This criterion reframes the design and deployment of quantum systems. It asks whether each watt of power, square millimeter of chip space, and hour of computer time is being used to deliver something meaningful.
Measuring Value, Not Just Capability
Usefulness requires a shift in how quantum progress is measured. Metrics like qubit count, gate fidelity, and decoherence times are essential but incomplete. They tell us how well a machine operates, not whether it solves the right problems.
Emerging measures like quantum volume, algorithmic benchmarking, and application-specific performance are gaining traction. These metrics evaluate how a system performs when applied to tasks of real commercial or scientific interest. They also consider how well hardware and software layers are integrated to deliver usable results.
It is a turning point for the industry. Companies and researchers must align around end-user value. That means asking what problems are worth solving and then designing systems that can tackle those problems at competitive cost and accuracy.
Integration with Classical Systems
Real-world usefulness rarely comes from quantum computers operating in isolation. Instead, it comes from hybrid workflows where quantum processors function as accelerators alongside classical systems. This model leverages the strengths of both architectures.
For example, a classical system might prepare data and validate results while the quantum component manages a complex, high-dimensional core computation. This co-processing approach maximizes the efficiency of quantum hardware while reducing the need for full quantum dominance.
Such integration also streamlines deployment. If quantum systems can be dropped into existing computer environments, adoption becomes much easier. Developers can build APIs, toolchains, and workflows that wrap quantum tasks in familiar containers. That is already happening in quantum cloud services and on-premises quantum integration projects.
Economics of Operation
Hosler’s point about value exceeding cost is especially relevant when considering the economics of quantum computing. Current systems are expensive to build and maintain. Dilution refrigerators, specialized control hardware, calibration protocols, and software stacks all contribute to high capital and operational expenses.
To become useful, quantum computing must deliver outputs that justify these inputs. That could mean simulating a complex protein interaction in hours instead of years, optimizing a supply chain with billions in savings, or solving cryptographic problems beyond classical reach.
Cost-benefit analysis will become part of procurement decisions. Enterprises will ask whether investing in quantum infrastructure yields unique, valuable, and timely results. If the answer is yes, quantum has a compelling case for adoption.
Usefulness Across Industries
Quantum usefulness will look different across domains. In materials science, it may mean accelerating the search for superconductors or battery materials. In healthcare, it could enable simulations that identify drug candidates without costly lab trials. In logistics, it may offer better routing under uncertain constraints. In finance, it might unlock novel forecasting methods or risk models.
Each use case has different requirements, such as tolerance for error, turnaround time, and model complexity. That means that quantum systems must be tuned to their application spaces. A general-purpose quantum computer is still a distant goal. In the meantime, task-specific usefulness is the key to momentum.
That’s why modularity and flexibility matter. Architectures that can be developed with workloads, integrate new subsystems, and pivot between qubit types will have an advantage. A machine built for chemistry in 2025 might need pivot optimization in 2027. Designing for this adaptability helps maintain usefulness across time.
The New Benchmark for Innovation
Usefulness is also a new benchmark for innovation. It places practical constraints on academic work and forces deeper collaboration between theorists, engineers, and end users. It also reshapes incentives. Breakthroughs in coherence time or qubit control are important, but they gain real weight when they translate to improved outcomes in a real task.
This emphasis on utility will drive better feedback loops. System builders will talk more with chemists, logistics managers, and financial analysts. Researchers will calibrate their benchmarks to reflect real workloads. Vendors will publish more results tied to outcomes rather than internal metrics.
It also sets the stage for policy and investment. Governments funding quantum programs will expect tangible benefits. Investors will ask how a company’s roadmap leads to commercially valuable output. Universities will reorient programs to train students not just in theory, but in application.
Delivering on the Promise
The race for quantum usefulness is now underway. It is no longer enough to demonstrate speedups or claim supremacy. Systems must solve problems that matter, under conditions that reflect the real world. They must integrate with broader workflows, deliver results within economic constraints, and do so reliably.
His comment offers a clear mandate. Quantum computing will earn its place not by chasing milestones, but by serving needs. When the value of its output consistently exceeds the cost of its operation, quantum will stop being an experiment and start being indispensable. That future is within reach, and usefulness is the path that leads there.