Quantum computing<\/a> gets treated as a faster form of computing. It is not. The machines use different physics and suit a limited group of problems. Nobody needs a quantum computer to send an email, run payroll or host a website. Researchers are building them for calculations where classical computers struggle as the number of variables rises: molecular modelling, some forms of optimisation, parts of financial modelling and, more awkwardly, the mathematics behind widely used encryption.\u00a0That last use already affects decisions being made today.<\/p>\nA qubit is not a better bit<\/h2>\n
Ordinary computers store and process information as bits. Each bit takes one of two values: zero or one. Billions of those bits sit behind everything from banking software to video streaming.<\/p>\n
Quantum computers use qubits. Handelsblatt starts with the same difference: classical computers work with bits, while quantum machines work with qubits. The similarity in the names hides how differently they behave.<\/p>\n
A qubit follows quantum mechanics. Before measurement, its state is described by a combination of possible outcomes rather than one fixed zero or one. Several qubits also interact through effects unavailable to ordinary bits.<\/p>\n
Three ideas appear again and again in descriptions of quantum computing: superposition, entanglement and interference. Superposition describes the quantum state of a qubit before measurement. Entanglement links qubits so their states become related. Interference changes the probability of different outcomes as a quantum algorithm runs.<\/p>\n
The common description that a quantum computer \u201ctries every answer at once\u201d makes the technology sound simpler than it is. A machine does not inspect every possible answer and then select the right one. The algorithm has to manipulate the quantum system so useful results become more likely when it is measured.<\/p>\n
Most calculations gain nothing from this. Classical computers already perform routine business work cheaply, reliably and at enormous scale. Quantum computing is aimed at problems where the structure of the calculation gives quantum methods an advantage.<\/p>\n
Keeping a quantum state alive is hard<\/h2>\n
The physics has been known for decades. Building a machine that controls it long enough to complete useful work has taken far longer.<\/p>\n
Qubits react to their surroundings. Heat, vibration and electromagnetic noise disturb their state and introduce errors. Some machines therefore run close to absolute zero. Others use lasers, vacuum systems and electromagnetic fields to hold atoms in place.<\/p>\n
A larger machine is not automatically a better machine. Thousands of unreliable qubits achieve little if errors accumulate before the calculation finishes. Researchers look at error rates, stability and the number of reliable logical qubits, not only the headline number of physical qubits.<\/p>\n
Error correction is the engineering problem behind much of the race. Several physical qubits are combined to produce one more reliable logical qubit. Doing so requires extra hardware and control. A machine with a large raw qubit count can still have very little usable computing capacity.<\/p>\n
Progress therefore looks less impressive than the headlines. Lower error rates and more reliable logical qubits tell us more than another record-sized processor.<\/p>\n
There is no agreed design<\/h2>\n
Quantum computing has not settled on one type of machine.\u00a0IBM and Google have concentrated heavily on superconducting circuits. IonQ uses trapped ions, where electrically charged atoms serve as qubits. Handelsblatt describes IonQ as a general-purpose quantum company that also distributes access through cloud partnerships. Other groups are working with neutral atoms, photons and different ways of controlling quantum states.<\/p>\n
D-Wave took another route. Its machines use quantum annealing, designed for a narrower set of problems rather than the range expected from a universal quantum computer. Handelsblatt lists traffic planning, logistics and production planning among the uses and names Volkswagen and BASF as customers.<\/p>\n
No architecture has won. Some designs are easier to control. Others scale differently or suit particular calculations better. The market may end up with several types of quantum machine rather than one standard design.<\/p>\n
Chemistry is one of the clearest uses<\/h2>\n
Molecules behave according to quantum mechanics. Modelling them accurately on a classical computer becomes harder as the number of particles and interactions grows.<\/p>\n
Researchers already use large computers and approximations to study molecular behaviour. Quantum machines offer another way to represent those systems.<\/p>\n
Drug research is an obvious example. Scientists screen large numbers of candidate molecules before laboratory work and clinical testing begin. Better modelling narrows the list earlier. It does not remove the laboratory or the clinical trial, but it changes how researchers choose where to spend time and money.<\/p>\n
Battery chemistry, catalysts, fertilisers and new materials raise similar problems. Researchers often want to know how molecules behave before producing them physically. Better simulations reduce part of the trial-and-error work.<\/p>\n
This explains why pharmaceutical, chemical and automotive companies are testing the technology before general-purpose quantum computing has reached the market.<\/p>\n
Logistics offers another route into business<\/h2>\n
A delivery company works with vehicles, routes, time windows and changing demand. A factory has machines, workers, materials and production deadlines. An electricity network has generation, consumption, weather and transmission limits.<\/p>\n
Add enough variables and the number of possible combinations becomes enormous.<\/p>\n
Classical software already solves many of these problems well. Quantum systems therefore have to beat mature software rather than fill an empty gap. A different mathematical method has little value unless it produces a better result, reaches the result faster or deals with a problem that classical systems handle poorly.<\/p>\n
D-Wave has concentrated on this type of work through quantum annealing. The company\u2019s commercial experiments in logistics and production also show how the first useful quantum systems may look: specialised machines used for particular calculations rather than replacements for ordinary computers.<\/p>\n
Finance has plenty to test, and plenty to compare against<\/h2>\n
Banks and asset managers run calculations with huge numbers of possible outcomes. Portfolio construction has to balance return, risk, liquidity and concentration. Derivatives pricing uses repeated simulations. Risk teams model markets under many different conditions.<\/p>\n
Quantum researchers have targeted all three areas.\u00a0Financial institutions also have some of the best classical computing systems available. They have spent decades improving models, software and hardware for exactly these problems. Any quantum system entering a bank has to improve on something that already works.<\/p>\n
A laboratory result is not enough. The bank needs a faster calculation, a lower cost or an answer that its current systems cannot produce within a useful period.<\/p>\n
Encryption brings the problem into the present<\/h2>\n
Public-key cryptography protects online banking, secure websites, software updates, corporate networks and digital signatures. Several widely used systems rely on mathematical problems that take classical computers an impractical amount of time to solve at large scale.<\/p>\n
Quantum computing changes part of that calculation.\u00a0In 1994, mathematician Peter Shor described an algorithm that gives a sufficiently large quantum computer a much faster way to solve some of the mathematical problems behind RSA and elliptic-curve cryptography.<\/p>\n
No machine operating today breaks modern encryption at that scale. Security teams are nevertheless replacing affected algorithms before such a machine exists.\u00a0The reason sits in the lifespan of the data.<\/p>\n
An attacker who steals encrypted files today does not need to read them today. The files can be stored and attacked later. Security researchers call the tactic \u201charvest now, decrypt later\u201d.<\/p>\n
Government material, intellectual property, research data and commercial secrets often remain sensitive for years. Information collected now may still have value when stronger quantum computers arrive.<\/p>\n
The migration also takes time. Encryption sits inside servers, certificates, identity systems, payment networks, industrial equipment, mobile devices and third-party software. Large organisations often do not have a complete record of every cryptographic system in use.<\/p>\n
Replacing one algorithm therefore becomes an inventory and replacement programme spread across years.<\/p>\n
The replacement runs on ordinary computers<\/h2>\n
Post-quantum cryptography does not require quantum hardware. It uses new algorithms designed to run on classical computers while resisting attacks from both classical and quantum systems.<\/p>\n
Standards bodies have spent years testing candidates and selecting replacements for vulnerable public-key methods. The work has already moved from research into implementation.<\/p>\n
Deployment is slower than publishing a standard. Operating systems, network equipment, cloud services, payment infrastructure, industrial systems and software all need to adopt the new algorithms. Some of those systems stay in service for ten or twenty years.<\/p>\n
A bank replacing cryptography across new cloud services has one job. A manufacturer with old factory equipment has another. The age and location of the technology decide how difficult the change becomes.<\/p>\n
Investors have arrived before the profits<\/h2>\n
The stock market has already priced quantum computing as a future industry. Analysts note that IonQ and D-Wave attract large expectations despite continuing losses. At the time of publication, IonQ shares had fallen by almost two-thirds from October 2025. Analysts also expected D-Wave to remain loss-making at least through 2027.<\/p>\n
The sector extends well beyond listed quantum specialists. IBM, Google, Microsoft and Amazon are building their own technology or giving customers access through cloud platforms. Suppliers of lasers, cooling systems, electronics, chips and measurement equipment sit around them.<\/p>\n
Most companies using quantum computing in future are unlikely to own the hardware. A pharmaceutical group that needs a particular molecular simulation does not need a quantum computer standing next to its servers. It needs access to one for the period of the calculation.<\/p>\n
Cloud delivery already provides that model. It also means the company with the strongest processor will not automatically own the strongest business. Software, access, support and links to existing corporate systems influence where customers go.<\/p>\n
Quantum computers are racing against classical computers<\/h2>\n
Quantum research does not happen while classical computing stands still.\u00a0Processors get faster. GPUs improve. Software improves. New algorithms reduce the amount of computing needed for some problems. A quantum machine therefore has to beat the best classical system available when the quantum product reaches the market, not the classical system that existed when the research began.<\/p>\n
Researchers often use the term quantum advantage for cases where a quantum machine performs a task outside the practical reach of a classical one. Laboratory demonstrations have already produced narrow examples.<\/p>\n
Commercial use asks for more. A company needs a real job done at a better cost, speed or level of accuracy. A calculation designed to prove that a quantum processor works is not the same as a calculation a company needs to run.<\/p>\n
The strongest proof of progress will not be another machine with a larger number attached to it. It will be a chemistry, logistics or financial problem that a company solves better with quantum computing than without it.<\/p>\n
AI arrived in a different order<\/h2>\n
Generative AI reached companies before most management teams had policies for it. Employees started using ChatGPT and similar tools first. Governance, security and procurement followed.<\/p>\n
Quantum computing gives companies more time, but some decisions arrive before the machines.\u00a0Security teams are already reviewing encryption. Pharmaceutical and chemical companies are building research partnerships. Banks are testing algorithms against existing models. Technology companies are placing bets on hardware designs that may take years to prove themselves.<\/p>\n
AI forced companies to react to a product that people could use immediately. Quantum computing asks for earlier planning around a technology that is still being built.<\/p>\n
AI and quantum computing do meet, but not in the way headlines suggest<\/h2>\n
Today\u2019s AI runs on classical chips, particularly GPUs and other specialist processors. Quantum computers are not direct replacements for that hardware. Researchers are testing quantum methods in machine learning, sampling and mathematical search problems. AI is also being used in quantum research to analyse experiments, control equipment and work on error correction.\u00a0None of this turns the combination into a shortcut to more intelligent AI. Each proposed use still has to outperform a classical method on a defined task.\u00a0\u201cQuantum AI\u201d is useful only when the two words describe an actual calculation.<\/p>\n
What to watch next<\/h2>\n
Error rates deserve more attention than raw qubit counts. Logical qubits show whether error correction is working well enough for longer calculations. Commercial trials show whether a machine earns its cost outside a laboratory.<\/p>\n
Security provides another measure. The move towards post-quantum cryptography will continue even if the date of a large fault-tolerant quantum computer moves backwards or forwards. Companies replacing long-lived systems now are making decisions for hardware and software that will still be in service years from today.<\/p>\n
The industry itself remains open. Ion traps, superconducting circuits, neutral atoms, photons and specialised annealing systems are all still in the race. The companies leading one measure today may not lead the market once customers start buying quantum computing for routine work.<\/p>\n
Five points are enough to keep the technology in perspective. Quantum computers do not replace ordinary computers. Raw qubit numbers do not tell us whether a machine works well. Chemistry, materials and selected optimisation problems give researchers some of the clearest commercial targets. Encryption creates work before large quantum machines arrive. And the decisive test remains simple: does the quantum system solve a real problem better than the best classical alternative?<\/p>\n
Most computing will stay classical. Quantum computing only needs to change a small group of hard calculations to affect drug research, industrial planning, finance and digital security. That is a narrower claim than the promise of a new computer age, and a more useful one.<\/p>\n
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