Quantum computing may change specific fields, but not every task, according to expert Shayan Majidy, who cautions that expectations should match current science and engineering limits. His message cuts through hype, laying out why quantum machines could help in targeted areas while leaving much day-to-day computing unchanged.
“What use is a quantum computer? Perhaps both more and less than you think,” said quantum computing expert Shayan Majidy.
Researchers and companies have poured money and talent into quantum projects over the past decade. The push centers on solving certain problems faster than classical computers can, such as modeling complex molecules or cracking old encryption methods. But error rates, hardware instability, and the scale needed for fault-tolerant systems remain major hurdles.
What Quantum Computers Are Good At
Quantum machines process information in qubits, which can represent more than one state at a time. This property can speed up a narrow set of problems. Scientists point to chemistry, materials science, and some math tasks as likely winners if devices reach stable performance.
The most cited example is breaking classical public-key encryption based on factoring large numbers. A quantum algorithm could, in theory, do that quickly. This risk has pushed governments and companies to adopt “post-quantum” encryption standards to protect future data.
- Simulating molecules and materials for new drugs and batteries
- Breaking certain legacy encryption schemes
- Speeding up parts of search, sampling, or optimization tasks
Near-term systems, often called noisy intermediate-scale quantum (NISQ) devices, are testing hybrid approaches. In these methods, a classical computer guides a small quantum circuit. Early results are promising in narrow cases but have not yet produced broad, repeatable gains over top classical methods.
Where Limits Show Up Today
Quantum hardware is fragile. Qubits lose information quickly due to noise from their environment. Correcting those errors requires many physical qubits to make one reliable logical qubit. That scale is far from reality for most platforms.
Past demonstrations have shown speedups on contrived problems, sparking debate over how useful those tests are in practice. Engineers are improving chip design, control electronics, and software, but the path to large, reliable machines is still long. Power use, cooling needs, and manufacturing challenges also slow progress.
Balancing Hype With Practical Use
Majidy’s framing reflects a shift in tone across labs and boardrooms. The field is moving from sweeping claims to targeted milestones and clear metrics. Investors and public agencies now ask whether a quantum method beats state-of-the-art classical solvers on real data, with costs and errors accounted for.
Some experts expect the first valuable wins to come from tasks like:
- Generating better starting points for hard optimization problems
- Sampling from complex probability distributions used in finance or physics
- Modeling small molecules with higher accuracy than classical methods can manage
Each of these aims depends on stable hardware and tight error control. Software progress matters too. Better compilers, error mitigation, and algorithm design can squeeze more out of limited devices. But none of this removes the need for fault-tolerant systems to reach the most famous targets.
Security And Policy Implications
The security world is already acting. Even if large-scale code-breaking machines are years away, data stolen today could be decrypted later. That “harvest now, decrypt later” risk has led to new encryption standards designed to resist quantum attacks. Companies are beginning long migration projects because cryptographic changes take time and must be tested carefully.
Public funding continues, but with more focus on benchmarks, workforce training, and responsible claims. Transparent reporting on qubit counts, error rates, and end-to-end performance helps set realistic expectations.
What To Watch Next
Key signals to track include steady drops in error rates, growth in high-quality qubits, and repeatable demonstrations that beat top classical baselines on tasks that matter outside the lab. Partnerships between hardware makers, software firms, and industry users may reveal early practical wins—or show where classical methods still dominate.
Majidy’s caution offers a clear guide for readers and decision-makers. Quantum computers are not a cure-all, but they are not a mirage. The near future will likely bring niche advantages in chemistry, materials, and certain math-heavy workflows. The big breakthroughs, including general code-breaking or sweeping speedups, will require fault-tolerant systems that do not yet exist.
For now, the takeaway is twofold: invest in careful experiments and post-quantum security, and judge progress by measured, verifiable results. That approach keeps expectations grounded while leaving room for genuine advances as the science matures.
Senior Software Engineer with a passion for building practical, user-centric applications. He specializes in full-stack development with a strong focus on crafting elegant, performant interfaces and scalable backend solutions. With experience leading teams and delivering robust, end-to-end products, he thrives on solving complex problems through clean and efficient code.
