Quantum Uncertainty

… the physics may be clear but the future … not so much

TL;DR: Quantum computing is becoming more plausible but further progress is unpredictable. Advances in error correction have strengthened the case that, at least in principle, large-scale machines may eventually be built, yet the range of important problems they will solve remains uncertain. For the moment, post-quantum cryptography and quantum sensing appear more immediate than any promised computational revolution.

Quantum computing occupies a curious position in public discourse. Many people know enough to have heard of qubits and quantum supremacy (but in any case do not worry, I will explain). Quite a few will have heard that quantum computers will break encryption. It is the poster child of emerging technologies: governments announce national strategies, venture capital flows, and technology companies release high-profile demonstrations. In the imagination it promises unbounded, low-energy compute and the capability to solve a wide range of complex problems that are beyond the reach of existing computational methods. The technology is, in this account, protean and its arrival is imminent.

As a frequent participant in discussions of emerging technologies I wish to apply a mild corrective. My aim is not to cast doubt on the merits of quantum computing but to place it in context, and perhaps create a little more space for discussion of other important technologies. Here is the @profserious assessment.

What & Why

The starting point is, necessarily, the science. Quantum mechanics is amongst the most successful scientific theories developed. It underpins semiconductors, lasers, magnetic resonance imaging and much of electronics. The challenge in quantum computing is not the physics, it is rather whether we can engineer devices capable of exploiting quantum effects at useful scale. This turns out to be a hard problem.

A conventional computer stores information in bits that take values of either 0 or 1. A quantum computer uses quantum bits, or qubits. Unlike ‘classical’ bits, qubits can exist in combinations of states and can be linked through the phenomenon known as entanglement. Groups of qubits can therefore represent and manipulate information in ways not available to classical systems.

Qubits behave less like switches and more like waves. As waves can reinforce or cancel one another, quantum states can interfere constructively or destructively. A quantum algorithm is a sequence of operations designed to shape patterns of interference. The objective is to amplify the probability of correct answers and suppress the probability of incorrect ones. When the system is measured, the desired solution is thus the one more likely to be observed. You are not required to understand this in order to understand the potential, or otherwise, of quantum computing.

What -is- important to understand is that these properties do not make quantum computers universally faster than conventional machines. For most computational tasks they are not. The interest arises because there appear to be specific classes of problem where quantum approaches may provide advantages. The most important candidates are optimisation, simulation of physical systems, cryptanalysis and -perhaps - some forms of machine learning. The crucial uncertainty is how large the space of economically important problems that admit substantial quantum advantage ultimately proves to be. This is an extremely challenging technical question in its own right and, arguably, the most important in the field.

We know some useful quantum algorithms exist, what we do not know is whether quantum advantage applies to a narrow collection of specialised problems or to a much broader range of economically significant activities. Advocates often assume many important algorithms remain to be discovered … and perhaps they do … it would be good to have some formal assurance. As the matter stands, it is possible that quantum advantage proves to be powerful but limited in scope.

You may have encountered the term ‘quantum supremacy’ (some researchers prefer ‘quantum advantage’). This is the point at which a quantum computer can perform a specific computational task beyond the practical reach of classical computers. Google’s widely publicised 2019 Sycamore experiment is generally considered the first credible demonstration of this milestone, although the precise comparison with classical approaches remains controversial. Specifically, IBM has rebutted the claim and the Sycamore-specific task has effectively been overtaken by classical methods, though Google has now published results showing that Willow has re-extended the lead. The important point to hold in mind is that supremacy is not the same as utility. Demonstrating that a quantum computer can outperform a classical computer on some specialised benchmark does not mean it can solve useful problems.

We will return to this, but we should first consider the engineering challenges in building a quantum computer.

Engineering Challenges

The primary obstacle is fragility. Quantum states are extraordinarily sensitive to disturbance. Heat, electromagnetic interference, manufacturing imperfections, cosmic rays and countless other environmental effects introduce errors into calculations. Whilst current quantum computers have error rates measured in 10ths or 100ths of a % per operation, useful computation may require millions or billions of operations; error correction is thus the defining challenge for the field. It is discussed further below.

Very large numbers of qubits must work together reliably and a useful quantum computer may require 100s of 1000s or even millions of physical qubits operating in concert, each of which must be manipulated with precision whilst remaining sufficiently isolated from its environment to preserve its delicate quantum state. Every qubit requires control systems, calibration, measurement and error management and small imperfections accumulate rapidly with consequences for scaling.

The environmental requirements are daunting. Superconducting quantum computers operate at temperatures measured in millikelvin (near absolute zero and colder than interstellar space) requiring elaborate cryogenic systems. Trapped-ion machines depend upon extremely stable electromagnetic fields and precision laser control. Neutral-atom approaches require arrays of atoms held and manipulated by sophisticated optical systems. Each architecture faces its own challenges of scaling, manufacturing, reliability and cost. Building a useful quantum computer thus involves far more than producing additional qubits. It requires constructing an entire system in which control electronics, software, error correction, networking, cooling and fabrication all function together within exceptionally narrow tolerances.

Until relatively recently, quantum error correction remained a largely theoretical concept. It was known that, in principle, many imperfect physical qubits could be combined into a smaller number of highly reliable logical qubits, but the difficulty was demonstrating that increasing scale in practice reduced overall error rates. In 2024 Google’s 105-qubit Willow processor produced the strongest evidence that this was the case. Error rates fell exponentially as larger error-correcting codes were used, confirming experimentally the prediction. Similar progress has been reported by Quantinuum in trapped-ion systems and by groups working with neutral atoms. Whilst it is important to emphasise that this does not directly yield a useful quantum computer it does, however, present a dimension along which engineering progress can be made.

The larger consequences are of course that a single highly reliable logical qubit may require 100s or even 1000s of physical qubits, depending on underlying error rates and the error-correction scheme employed. A machine capable of tackling commercially significant problems may therefore require millions of physical qubits. This is why announcements of 100-qubit or even 1000-qubit systems should be interpreted with some caution, whilst they represent progress they remain much closer to the beginning of an engineering process than its end.

Uses

The most straightforward way to understand what this means in practice is by way of cryptography. Much of the public attention surrounding quantum computing derives from ‘Shor’s algorithm’. Shor showed that a sufficiently capable quantum computer could factor large integers and solve discrete logarithm problems exponentially faster than known classical methods. Since the established methods of encryption - RSA, Diffie-Hellman and elliptic-curve cryptography - rely upon the practical difficulty of these problems, a sufficiently powerful quantum computer would undermine much of today’s public-key infrastructure. That is, it threatens the technology that underpins secure digital transactions and communications undertaken without first sharing a secret key.

Whilst the mathematics is well established, engineering yet again gets in the way. A widely cited estimate published by Craig Gidney in 2025 suggests that breaking a standard RSA-2048 key would require 897,864 noisy physical qubits running for 4.96 days. This represents, somewhat surprisingly, progress, prior estimates exceeded 20 million qubits. It also illustrates the scale of the challenge. Current advanced machines contain 100s of qubits, not 100s of 1000s. This is a gap of several orders of magnitude suggesting something more than engineering refinement is required.

Predictions vary considerably, but a useful fault-tolerant quantum computer before the early 2030s appears optimistic. A machine capable of threatening current cryptography is more plausibly a problem for the second half of the next decade or beyond.

Cryptography may not be the most important application of quantum computing, though, for reasons discussed below, it may be the most immediately consequential. The larger economic prize lies in optimisation. Many practical decisions involve searching very large spaces of possible solutions. Thus, airlines seek efficient schedules for aircraft and crews, manufacturers optimise production processes, logistics firms plan routes and warehouse operations, energy systems balance generation, storage and demand, financial institutions optimise portfolios subject to multiple constraints, universities build timetables, and so on. These problems are difficult because the number of possible solutions often grows extremely rapidly with problem size.

The so-called ‘travelling salesman problem’ provides the classic illustration. Given a collection of cities, what is the shortest route that visits each city exactly once and returns to the start? For small numbers of cities the answer is straightforward to determine. For large numbers of cities, the number of possible routes grows very quickly, soon exceeding any practical ability to examine them individually. These combinatorial challenges appear throughout science, engineering, planning and resource allocation.

Quantum systems appear naturally suited to exploring such complex spaces. Algorithms such as the Quantum Approximate Optimisation Algorithm, or QAOA, have therefore attracted significant interest. The difficulty is that convincing demonstrations of practical quantum advantage remain elusive. On current hardware, the strongest results generally show quantum methods approaching the point of becoming competitive with leading classical techniques rather than clearly outperforming them. Resource-estimate analysis paints a less optimistic picture: achieving a crossover against state-of-the-art classical heuristics on even small optimisation tasks would require fully fault-tolerant hardware far beyond anything available today, or likely in the mid-term. The possibility remains that future quantum computers will transform optimisation but the current evidence does not, however, justify assuming that outcome.

Simulation of physical systems presents a stronger case. This was Richard Feynman’s original motivation when proposing quantum computation in the early 1980s. Nature itself is quantum mechanical. Molecules, materials, chemical reactions and many fundamental physical processes are therefore inherently difficult to represent using classical computation. Quantum computers may provide a more natural framework for such problems. Potential applications include catalyst design, battery chemistry, advanced materials and aspects of pharmaceutical discovery. You may think this is hardly surprising, of course the question remains: how much quantum hardware will be required to realise these models in practice.

There is certainly no shortage of discussion concerning quantum machine learning, quantum neural networks and quantum-enhanced AI. There are theoretical reasons to believe that quantum methods may assist certain tasks involving optimisation, sampling, probability distributions and high-dimensional mathematics. Yet practical demonstrations remain limited and reviews of the literature continue to find little consistent evidence that quantum machine-learning approaches outperform the best classical techniques on realistically sized problems. This does not mean useful applications may not emerge but, as it stands, quantum machine learning should be regarded as interesting but highly uncertain.

Quantum Threat

For most organisations the immediate practical quantum issue is not in fact computation but cryptography. Migration towards post-quantum cryptography is already under way and this makes sense. The United States finalised its first post-quantum cryptographic standards in 2024. The UK’s National Cyber Security Centre has established a phased migration programme extending to 2035. Importantly, this date is not a prediction of when quantum computers will break encryption. It is an explicit recognition that replacing cryptographic infrastructure across large organisations can take a decade or more.

The practical significance of the threat depends not merely on when a capable quantum computer appears but on how long information retains value. Much commercial information becomes largely worthless within a few years. A good deal of it within minutes. Operational plans, business negotiations and routine communications rarely require protection for decades. Other information is different. Intelligence sources, defence information, diplomatic communications, health records and certain categories of scientific and industrial data may retain value for very long periods. Such information could plausibly be subject to a harvest-now-decrypt-later strategy in which encrypted material is collected today for future decryption. The sensible response is therefore not panic but classification, understanding which information requires long-term protection and prioritising accordingly.

Quantum communications receives considerable attention in this context, particularly quantum key distribution. Intercepting a quantum communication channel necessarily disturbs the quantum state and reveals the presence of an eavesdropper. Whether this addresses an important practical security problem is another matter altogether.

The National Cyber Security Centre (NCSC) has consistently taken a sceptical position which @profserious shares. The reason is straightforward, successful cyber attacks do not generally depend upon intercepting encrypted communications in transit. They exploit vulnerable devices, compromised users, software flaws or poor operational practice. The endpoints are usually far more vulnerable than the communication channel. Replacing an already secure communication link with a quantum-secure link may therefore do little to improve overall security. Security is a systems problem and, for most organisations, post-quantum cryptography is the correct response to the ‘quantum threat’ rather than large-scale deployment of quantum key distribution.

Opportunities

The most interesting quantum technology over the next period is not, I would contend, quantum computing at all. Quantum sensing exploits the same sensitivity that makes quantum computers so difficult to build. A quantum state that is easily disturbed becomes, from another angle, a precise measuring instrument. This opens opportunities in navigation, timing, healthcare, geophysics, infrastructure monitoring, defence and national security.

The United Kingdom is particularly well positioned in this area. Researchers at the Birmingham demonstrated a field-deployable quantum gravity gradiometer capable of detecting buried tunnels and underground infrastructure through very small variations in local gravitational fields. The associated spin-out, Delta g, is attempting to commercialise what is effectively a form of underground mapping. Quantum magnetometers are entering clinical trials for advanced brain imaging. Atomic clocks and inertial sensors offer the prospect of navigation systems less dependent upon vulnerable satellite infrastructure. Unlike quantum computing, these applications do not require millions of error-corrected qubits. A relatively small number of highly controlled quantum systems can produce useful capabilities.

This changes the policy conversation somewhat - quantum sensing is not a distant possibility. It is an area where the UK’s strengths in physics, metrology, instrumentation and photonics align naturally with practical applications. The economic and strategic benefits may arrive much faster and be more ‘sticky’ than those associated with universal fault-tolerant quantum computers.

The UK’s position is strong although the level of international competition is high (see for example my article on China’s 5-year Plan) . The National Quantum Strategy committed £2.5 billion over 10 years. The National Quantum Computing Centre at Harwell opened in 2024. New hubs support research in computing, sensing, networking and navigation technologies. UK universities remain among the world’s strongest contributors to quantum science, and the country attracts a disproportionately large share of global private investment. The challenge is, of course, not scientific excellence, it is rather commercialisation.

The UK has repeatedly demonstrated an ability to generate promising technologies and innovative firms, something we should not underestimate as national capability. It has however been less successful at scaling them into globally competitive businesses. The acquisition of Oxford Ionics by IonQ in 2025 illustrates both the quality of UK innovation and the continuing difficulty of retaining ownership of successful scale-ups. Equally important is the question of demand. New technologies require sophisticated customers willing to experiment, procure and deploy emerging capabilities. The UK possesses strong potential early adopters in pharmaceuticals, financial services and government. Public procurement can play an important role by acting as an anchor customer for emerging technologies and helping firms cross the difficult gap between demonstration and deployment. Long-term success may depend as much on creating demanding domestic users as on funding excellent research.

Conclusions

Several conclusions seem reasonable. In essence quantum technologies are advancing rapidly but the distance to traverse is very large. Advances in error correction increases confidence that large-scale quantum computation may eventually be achievable. Fault-tolerant quantum computers remain plausible but not imminent, and timescales could reasonably extend to a decade or longer.

Migration to post-quantum cryptography is a sensible precaution and should, as a matter of course, be planned into digital infrastructure renewal. Quantum sensing will likely prove economically significant sooner than quantum computing. The UK’s challenge, as always, is less one of scientific and innovative capability than of commercial execution.

Beyond that, there is no certainty. We have no reasoned basis for ascertaining the set of problems that might benefit computationally from quantum advantage. It could prove to be extensive or narrow. Machine learning may, for example, be transformed, marginally improved or largely unaffected. Timelines may accelerate or disappoint, engineering progress is rarely linear. The sensible position is neither enthusiasm nor wholesale scepticism … just do not bet the farm.