The Main Point
A computer solving a difficult problem and a company gaining business value are different achievements. The Financial Times’ analysis of quantum computing makes that distinction central: scientific progress expands the possibilities, but useful and economically justified applications still require their own evidence. Businesses need to ask not only how powerful a machine is, but what it improves, what the complete process costs, and who captures the resulting value. For Georgia, the practical implication is to build knowledge, problem-definition skills and evaluation capability before treating hardware ownership as the starting point. [1]
Consider an illustrative meeting at a Georgian distribution company. A manager is offered a pilot of a quantum-based solution intended to improve delivery routes. The presentation describes impressive technology and promises faster solutions to difficult problems. Yet the first useful question is much simpler: where does the company currently lose money? Is the difficulty in the data, late orders, vehicle allocation or the speed of the calculation itself? Without an answer, the company risks applying its most sophisticated tool to a problem whose principal cause lies elsewhere.
This is not an argument against quantum computing. It is an argument for understanding the conditions under which an advantage would matter. If a new method accelerates one small component of an existing system, the improvement in the complete service may be negligible. If it makes a previously inaccessible, high-value problem tractable, a different economic assessment is needed. In both cases, the decision should begin with the company’s problem rather than the technology’s name.
Scientific success is not a completed business case
In the Financial Times’ 26–27 September 2026 issue, Richard Waters and Michael Peel describe a field in which technical progress and commercial uncertainty coexist. Their interviewees discuss the development of quantum machines, competing technological approaches and the search for practical applications. The article distinguishes a demonstration of quantum advantage on a particular task from a commercially useful product. Optimism does not close that distance; closing it is the work that follows. [1]
The underlying proposition is to exploit different physical capabilities, rather than simply build a faster version of a conventional computer. Quantum computers use qubits whose states follow quantum rules. The explanatory graphic on the FT’s printed page 13 sets out the distinction between conventional bits and qubits. It does not establish that every business program will run faster. Finding a suitable task and algorithm is precisely one of the article’s unresolved commercial questions. [1]
A separate scientific example comes from Rajeev Acharya and colleagues’ research, published in Nature in 2025. They demonstrated lower errors in quantum memory as an error-correction system increased in size under appropriate conditions. That is a technical result, not a measurement of a company’s profit. [2] Maintaining the distinction matters: improved memory reliability may be a prerequisite for useful computation, but it should not be credited with a commercial outcome the research did not measure.
A business assessment therefore needs to connect several independent questions. Can the computation be performed reliably? Does it outperform an appropriate classical alternative? Is the difference worth the full cost of obtaining it? These questions are related, but a positive answer to one does not settle the others. A scientifically important experiment may not yet justify deployment, while a method with a limited range of applications could still be valuable to a particular customer.
The first valuable application may be a narrow one
The FT places the modelling of natural quantum systems near the centre of its discussion. Interviewees consider the prospect of contributions to drug discovery, new materials and better batteries. These are possible application areas, not evidence that profitable quantum services already exist throughout those industries. The important distinction is that a specialised calculation could address a small but decisive part of a much larger economic process. [1]
For illustration, a materials developer might have no reason to replace all its software. A better estimate of a particular chemical property could be useful if it makes subsequent experiments more targeted. In that scenario, the value of the calculation should be judged against the decision it changes, not just the computer time it consumes. A better prediction still does not constitute a better product: further testing, production and customer demand remain separate questions.
On optimisation, the FT presents a less uniform picture. Some interviewees anticipate broad commercial opportunities, while others argue that existing methods already provide sufficiently good approximate answers for many practical problems. When a company needs a good plan delivered on time, a more precise answer is not necessarily a more valuable one. The difference must appear in a relevant outcome: lower costs, better service or the removal of a constraint that the available alternative cannot address. [1]
Neither side of that disagreement should be presented as having settled every application. The commercial expectations described in the article include the assessments of technology suppliers and investors. They help explain why a direction is being explored, but they cannot substitute for an independent comparison. The same caution applies to the possible relationship between quantum computing and AI. The FT discusses a longer-term opportunity; it does not establish that quantum technology is a necessary or economically justified component of any AI system today. [1]
Creating value and capturing it are different problems
One of the article’s most important business questions concerns who will retain the benefits of quantum computing. If a new method helps a pharmaceutical or chemical company achieve a better result, what share of the value will flow to the computing supplier? The FT discusses potential profit-sharing agreements, but as possible business models and expectations. They are not a verified answer to how much the sector will earn. [1]
Such a distribution would depend on whose contribution is difficult to replace. A hardware manufacturer, an algorithm developer, a data owner and a final-product manufacturer are not the same participant. In an illustrative collaboration, the customer might possess the data and domain knowledge without which the computation cannot become a business application. The supplier might possess a capability that is difficult to obtain elsewhere. An agreement would need to reflect these different contributions, not merely the participants’ shared enthusiasm.
For the same reason, a technology supplier’s revenue and its customer’s productivity gains should be examined separately. Funding an experiment shows a willingness to pay for learning. By itself, it does not show that the resulting solution creates additional value in everyday operations. Buying research can be entirely reasonable, but a research project and an established service need different objectives, budgets and evaluation criteria.
For a Georgian technology team, this raises an interesting question to investigate. Must participation involve building the hardware, or could value lie in defining an industry problem, verifying a result and integrating it into a workflow? This is an opportunity hypothesis, not an established estimate of a Georgian market. Testing it requires a customer with a genuine problem and a comparison that demonstrates what the new approach improves relative to available alternatives.
The decisive test is a credible comparison
In the FT, a Quantum Motion representative emphasises that an exceptional technology is not automatically worth building at any cost. [1] As additional official context, DARPA’s Quantum Benchmarking Initiative defines utility-scale operation in terms of computational value exceeding cost. Its 2033 horizon is a target to investigate, not a guaranteed outcome. [3] For a business assessment, the important idea is the relationship between value and cost, rather than converting a programme’s date into a delivery promise.
A practical comparison cannot stop at the number of seconds spent on a device. Preparing the problem, transferring data, checking results, repeating calculations and using employees’ time all belong in the total cost. The alternative must also be credible. Comparing a quantum approach with an obsolete or poorly configured classical program could create the appearance of a technological advantage when better planning or an ordinary software upgrade explains the improvement. This is an evaluation approach proposed in this article, not a description of a test conducted for it.
Consider a purely illustrative annual calculation. An existing process costs GEL 100,000. A proposed solution is assumed to reduce that underlying cost by 8%, saving GEL 8,000. If its full additional annual cost is GEL 12,000, total expenditure becomes GEL 104,000. Under those assumptions, the technical improvement comes with an additional net cost of GEL 4,000. These are neither market prices for quantum services nor data from a Georgian company. They simply distinguish gross savings from the net financial result.
The project could also have learning value: a team might understand an algorithm, identify a data problem or prepare to examine a future capability. Continuing the experiment could then be reasonable, but on a different basis and within an explicit limit. Learning benefits do not turn current expenditure into already-realised operating savings. An organisation should be able to say what it is buying: an improvement today, knowledge or an experiment addressing an uncertain future.
It is useful to agree at the start of a pilot what findings would change the decision. Stopping when an approach fails to meet requirements for accuracy, timing or total cost is not an attempt to conceal failure. Discovering that it reproduces a classical method’s performance at greater expense is also information. If an advantage appears only under special conditions, the next question is whether those conditions exist in the actual workflow. Research then remains a source of accumulated knowledge rather than a presentation designed only to display success.
Readiness begins before hardware ownership
Additional technical documentation shows that experimentation need not require owning a machine: Amazon Braket describes access to simulators and remotely available quantum devices. [4] Access does not prove an advantage. Nevertheless, the distinction matters for a learning programme: a university can consider modelling and comparison without making the construction of hardware its initial research objective.
One possible BTU learning project would examine a single industry problem through several approaches. Students would first describe the business requirement, then develop a classical baseline and only afterwards assess the case for a quantum or hybrid approach. The final work would need to explain what improved, what did not and why, rather than merely demonstrate that a program ran. This is a proposal for educational design. The analysis does not claim that such a course has been delivered or that its effectiveness has been established.
Security is another strand of quantum readiness that should be treated separately from commercial computation. In August 2024, NIST released its first three finalised post-quantum cryptography standards and encouraged organisations to begin updating their systems. [5] This is additional official context, not an outcome of the FT’s application examples. Implementing quantum-resistant cryptography does not require buying a quantum computer; the issue is the protection of existing digital systems.
A company considering this work could begin by establishing which important data and services depend on cryptography, who manages the relevant systems and how they can be updated. Technical choices require specialist assessment. The managerial distinction is that testing a potential computational capability and upgrading security are not the same project. Combining them in one promise or an undifferentiated budget would make it harder to see which risk is being reduced and which benefit is being sought.
BTU Researchers’ Assessment
The assessment developed for this BTU analysis is that quantum technology should be evaluated across three connected outcomes: reliable computation, a real improvement in the company’s work and economically justified use. This is a proposed practical framework, not an established international standard. Growth in a technical metric alone cannot show whether a customer-relevant result has changed; the sale of an experiment cannot establish sustainable demand for a service.
The contribution of this approach is not to choose optimism or scepticism as a general position. It is to match different decisions with different evidence. Learning may justify a clearly defined research question. Deployment requires repeatable results. Broader use requires reliable service and a defensible full-cost assessment. An organisation can therefore study quantum computing actively while having sound reasons not to use it in its everyday operations.
Key Findings
- An experimental demonstration of quantum advantage and a profitable business application require different kinds of evidence. [1]
- Modelling natural quantum systems is discussed as a promising direction, but a possible application is not an already-realised commercial result. [1]
- A specific project should be compared with an appropriate classical alternative using total cost and the quality the workflow actually requires.
- Customer benefits and the technology supplier’s revenue are separate questions. [1]
- Developing knowledge, evaluation skills and targeted experiments is a proposed path for Georgia, not a description of measured local success.
Why This Matters for Georgia
A practical discussion should not start with an unsupported assumption that every Georgian sector urgently needs a quantum solution. Defining companies’ actual problems and developing specialists who can test technological claims would be more useful starting points. A university and a business could collaborate around a specific task, suitable data and an agreed method of measurement. This analysis does not use representative data on quantum adoption or realised returns in Georgian companies; the local opportunities remain hypotheses to investigate.
Conclusion
The significance of a quantum computer should not be assessed solely by how unusual its operation is. Its business value will emerge where a new computation improves an important decision at an acceptable cost. For Georgia, readiness can mean building the knowledge needed to recognise a genuine opportunity rather than guessing the date on which the future will arrive. Respect for scientific progress and rigorous scrutiny of business outcomes are compatible. Keeping both is the discipline that matters.
Data and Main Sources
The main journalistic basis is [1]; [2]–[5] are additional primary sources. Georgian examples and the cost model are illustrative. Sources checked: 26 September 2026.
[1] Richard Waters and Michael Peel – “Quantum tries to turn ‘magic’ into money”. Financial Times, 26–27 September 2026, printed p. 13 (PDF p. 12).
Print source; an exact public article URL was not verified.
[2] Rajeev Acharya et al. – Quantum error correction below the surface code threshold. Nature 638 (2025), 920–926; arXiv:2408.13687 (2024).
https://arxiv.org/abs/2408.13687
[3] DARPA – Quantum Benchmarking Initiative (QBI). Programme description.
https://www.darpa.mil/research/programs/quantum-benchmarking-initiative
[4] Amazon Web Services – What is Amazon Braket? Developer Guide.
https://docs.aws.amazon.com/braket/latest/developerguide/what-is-braket.html
[5] NIST – NIST Releases First 3 Finalized Post-Quantum Encryption Standards. 13 August 2024; updated 29 August 2025.
This material is analytical and educational. It does not constitute technology-specific, financial, investment or organisation-specific professional advice.
Prepared by the academic team of Business and Technology University and the BTUAI Research Team, Tbilisi, Georgia.