The quantum computing space is no longer defined solely by theory and potential. It is now confronted with an urgent, pragmatic question: what does it take to make a quantum computer genuinely useful? Erik Hosler, a technical strategist in photonics-based quantum architecture, highlights the growing need to separate engineering novelty from measurable return on investment. He brings a level-headed perspective to a field frequently overtaken by hype.
Quantum computing’s allure lies in its promise to solve complex problems that stump even the most powerful classical machines. But turning that promise into reality requires far more than performing calculations faster. It demands utility in real-world solutions that create meaningful value, which are economically scalable, and that serve a purpose beyond proving what is possible in a controlled lab setting.
From Possibility to Practicality: A Shifting Benchmark
There’s a growing realization in the quantum community that flashy demonstrations are not enough. The ability to perform a calculation that a classical computer cannot, known as “quantum supremacy,” may capture headlines, but it doesn’t automatically imply real-world relevance. Google’s 2019 experiment, in which its Sycamore processor ran a random number sampling task in 200 seconds that would take classical computers thousands of years, is a classic example.
By contrast, utility demands a stricter test: Can the quantum computer solve a valuable problem better or faster than any alternative and at a reasonable cost? That test turns attention to practical metrics, such as how much computation power is being produced, what kinds of problems are being tackled, and whether the outcomes generate measurable benefits. These are the new performance indicators for a technology long measured by possibility.
Defining the Economic Equation
At the heart of the discussion lies a deceptively simple but powerful principle. Usefulness is not just about solving problems. It’s about solving them at a cost that makes sense. Erik Hosler explains,
“The value of the computations it performs exceeds the cost to build and operate the computer.”
This framing cuts through technical jargon and sets a clear, market-aligned benchmark. It shifts the focus from theoretical capability to economic deliverability.
A quantum computer might be able to model complex molecules or optimize transportation coordination at astonishing speeds, but if it costs billions to run and maintain, its real-world impact remains limited.
This definition has profound implications. It calls on engineers, researchers, and investors to measure progress in terms of qubit count or gate fidelity as well as the ability to derive cost-effective insights. It also demands closer collaboration between quantum technologists and industry leaders to identify high-value, solvable problems.
Qubits, Noise, and the Challenge of Scale
A central challenge to reaching utility lies in the nature of the qubits themselves. Unlike classical bits, qubits are incredibly sensitive to environmental noise. Even the slightest fluctuations in temperature or electromagnetic interference can corrupt calculations. That is why most quantum systems today are engineered at cryogenic temperatures just above absolute zero, a costly and complex undertaking.
What’s more, because qubits are so error-prone, we currently require thousands of physical qubits to construct a single logical qubit, one that can reliably function in an extended computation. This error correction process introduces immense overhead. To reach the point where useful computations can be performed, many believe we’ll need machines with at least one million qubits.
That’s not just a technical leap; it’s an infrastructural one. We’re talking about manufacturing, controlling, and stabilizing millions of quantum elements in parallel, all while keeping them coherent for long enough to complete complex algorithms. Few technologies in history have required such an orchestrated scale-up.
Supremacy vs. Usefulness: Why the Difference Matters
There’s a growing consensus that the next era in quantum will not be marked by bold claims in academic journals but by small, specific wins in industry. For example, simulating the structure of a specific molecule for drug development or optimizing a multi-variable coordination network for a shipping company are use cases that might not make headlines but could produce tangible ROI.
In this sense, usefulness is about aligning technical progress with business value. It’s about translating quantum capacity into computational efficiency and cost savings, or revenue generation.
The Economics of Scale and Photonic Strategy
PsiQuantum, the company where he leads photonics exploration, is betting on light, no matter what, as the basis for scalable quantum computing. Their approach uses silicon photonics to generate and manipulate photons as qubits. One of the theoretical advantages is that photonic systems may be easier to scale and potentially operate at higher temperatures than their superconducting counterparts, thereby reducing overhead.
Photonic architectures also lend themselves to leveraging existing semiconductor fabrication technologies. It makes it easier to envision mass production, chip integration, and potential cost containment, all critical factors if a quantum computer is to become not just powerful but practical.
Still, it’s worth noting that no architecture has yet achieved the scale or reliability needed to meet this standard of usefulness. Every approach, whether photonic, ion-trap, or superconducting, faces a steep road ahead in proving not only performance but also financial and operational viability.
Measuring Value: From Molecules to Markets
Where will the first incredibly useful quantum applications emerge? Most experts point to chemistry and materials science as early candidates. This domain is notorious for systems that are naturally quantum and notoriously difficult for classical computers to simulate. A quantum computer capable of modeling new catalysts, drugs, or battery materials could save companies years of trial-and-error experimentation.
Finance is another promising area. Portfolio optimization, risk analysis, and fraud detection are all computationally heavy and data-sensitive, ideal candidates for quantum enhancement. In such cases, even a small edge in accuracy or speed could translate into a massive economic advantage.
But again, the technology must be both accurate and affordable. That’s the essence of the argument: utility only exists when there is a clear return on investment.
Closing the Gap Between Promise and Payoff
Quantum computing sits at a rare intersection of lofty expectations and hard science. The journey from concept to utility will not be paved with headlines but with hard-won validations, experiments that yield value, algorithms that deliver real insight, and systems that scale responsibly.
This emphasis on utility sets a refreshing tone in an industry that has often celebrated novelty over need. This economic lens demands a more rigorous accounting of progress and invites a closer partnership between technologists and end users.
The speed of computation alone won’t judge the next wave of breakthroughs, but whether the machine in question justifies its cost, not only in technical effort but in actual value delivered. That’s the real reality check the quantum world needs.
