The quantum computing industry has been in “five years away from commercial viability” territory for about a decade now. Each year brings announcements of new qubit counts, improved coherence times, and breakthrough algorithms. Each year also brings approximately zero new practical applications that outperform classical computers on real business problems.

I’ve been following quantum computing developments since the early research phases, watching as the field transitioned from pure academic pursuit to commercial venture capital darling. The disconnect between what’s being promised and what’s being delivered has never been wider.

The technical progress is real. Today’s quantum computers have hundreds of physical qubits compared to handfuls five years ago. Error rates have improved significantly. Coherence times are longer. New qubit technologies (trapped ions, photonic qubits, topological qubits) offer different trade-offs between stability and scalability. These are genuine advances.

But none of these advances have translated into quantum computers solving problems that matter to businesses. The applications that theoretically benefit from quantum computing—drug discovery, materials science, optimization, cryptography—remain stubbornly resistant to practical quantum solutions.

The central challenge is error correction. Quantum states are fragile and degrade rapidly through interaction with their environment. Current quantum computers produce errors frequently enough that results from complex calculations are unreliable. Quantum error correction codes exist in theory, but implementing them requires massive overhead—potentially thousands of physical qubits to create a single reliable logical qubit.

This means that even as companies announce “1,000 qubit quantum computers,” the number of error-corrected logical qubits available for actual computation remains in single digits or low tens. And most interesting problems require hundreds or thousands of logical qubits to show advantage over classical approaches.

The algorithms that might benefit from quantum computing also remain largely theoretical. Shor’s algorithm for factoring large numbers gets enormous attention because of its implications for cryptography, but running it at a scale that threatens current encryption requires error-corrected quantum computers far beyond current capabilities. Estimates suggest we’re still decades away from quantum computers that could break RSA encryption.

Optimization algorithms like QAOA (Quantum Approximate Optimization Algorithm) have been demonstrated on small problems, but scaling them to commercially relevant problem sizes encounters the same error correction barriers. The problems you can solve on current quantum computers are ones that classical computers solve trivially.

The drug discovery and materials science applications are particularly frustrating. In theory, quantum computers could simulate molecular interactions more efficiently than classical computers, enabling faster discovery of new drugs or materials. In practice, the molecules you can accurately simulate on current quantum computers are so simple that classical simulation methods work fine. The complex molecules that would benefit from quantum simulation are far beyond current capabilities.

Some companies are exploring “quantum-inspired” classical algorithms—taking insights from quantum computing research and implementing them on classical hardware. These sometimes provide real benefits, which is great, but it undermines the value proposition of quantum hardware itself.

The business model for quantum computing companies is increasingly problematic. Most revenue comes from consulting, education, and cloud access to quantum computers for research purposes. Actual commercial use cases that generate value remain vanishingly rare. This is sustainable while venture capital continues flowing, but it’s not a path to independent viability.

Several quantum computing companies have gone public or been acquired at high valuations based on future potential. The gap between these valuations and current commercial reality is enormous. At some point, investors will demand returns based on actual utility rather than continued promise of future breakthroughs.

There’s also a skills gap issue. The number of people trained in quantum computing far exceeds the number of problems where that knowledge is currently useful. Universities are producing quantum computing specialists, companies are hiring them, but there’s limited practical work for them to do beyond research and development.

This creates a peculiar dynamic where organizations invest in quantum computing teams primarily to signal technical sophistication rather than to solve specific business problems. It’s status-seeking behavior dressed up as technological investment.

The infrastructure requirements for quantum computing remain challenging. Most quantum computers require near-absolute-zero temperatures, extensive electromagnetic shielding, and vibration isolation. These aren’t systems that will sit in a corporate data center. Cloud access is the realistic deployment model, but that creates latency and data security considerations for any time-sensitive or confidential applications.

Some quantum technologies (photonic quantum computing, room-temperature quantum processors) promise to eliminate extreme cooling requirements, but these approaches have their own scalability challenges. There’s no clear path to quantum computing that’s both powerful and practical for widespread deployment.

The timeline for useful quantum computing keeps extending. Five years ago, the industry suggested commercial applications would emerge by 2025. Now the timeline has shifted to 2030 or beyond for most practical applications. This pattern of continuously receding timelines is familiar from other emerging technologies that eventually failed to deliver.

That’s not to say quantum computing won’t eventually work. The physics is sound, the research is advancing, and sustained investment may eventually overcome current limitations. But the gap between current capabilities and commercially useful applications is much larger than industry messaging suggests.

For businesses evaluating quantum computing investments, the realistic position is to monitor developments while remaining skeptical of near-term commercial claims. Experimental access to quantum computers for research purposes might be worthwhile for organizations with relevant technical expertise, but expecting business value from quantum computing in the next 3-5 years is likely unrealistic.

The quantum computing industry would benefit from more honest communication about current limitations and realistic timelines. Continuous hype without delivery erodes credibility and eventually turns off investors and potential customers. A more measured approach—celebrating technical progress while acknowledging commercial challenges—would better serve long-term industry development.

In the meantime, classical computing continues advancing. Moore’s Law may have slowed, but specialized classical processors (GPUs, TPUs, neuromorphic chips) continue improving performance for specific applications. The performance gap that quantum computers need to overcome keeps growing as classical alternatives improve.

Quantum computing remains an important area of research with potential long-term implications. But in 2026, it’s still primarily a research field rather than a commercial technology. Organizations should invest accordingly—enough to maintain awareness and capability, not so much that significant business value depends on near-term quantum computing breakthroughs that remain unlikely to materialize.