For years, the story around quantum computing and cyber security has been relatively simple: one day, sufficiently capable quantum computers may be able to break much of the public-key cryptography that protects the internet, financial systems, software updates and sensitive communications.

That threat is real. But it is no longer the whole story.

A newer development is beginning to matter:

AI is helping researchers test cryptographic systems faster and more aggressively than before. It is not replacing cryptographers, and it has not “broken post-quantum cryptography.” But it is beginning to change the speed at which weaknesses can be discovered, checked and acted upon.

That should change how we think about the transition to quantum-safe security.

The headline is not that PQC failed

The immediate trigger for this discussion was HAWK, a digital-signature scheme being considered in NIST’s additional post-quantum cryptography process.

Last month, Anthropic reported that its Claude Mythos model helped researchers find a significant structural weakness in HAWK. However, Shortly afterwards, the HAWK team withdrew the scheme from NIST consideration. It will not become a standard or be deployed as one.This story may sound a bit dramatic, but the right interpretation is not “post-quantum cryptography has failed.”

It is almost the opposite.

In other words, a weakness being found before deployment is what a healthy standards process is always meant to achieve.

NIST’s established post-quantum standards are not HAWK.

In 2024, it published ML-KEM as its principal key-establishment standard, alongside ML-DSA and SLH-DSA for digital signatures. NIST says the HAWK result does not affect those final standards.

The bigger story is the speed of review

The more important development though, is not simply that HAWK had a weakness. Cryptographic candidates have always been challenged, and some have always failed.

The difference is that an AI system may now help researchers generate ideas, search mathematical structures, test attack paths and connect possibilities at a pace that a small team of specialists could not easily match on its own.

Think of cryptography as designing a lock for an extremely valuable vault. Before installing that lock everywhere, you want the most skilled lock-pickers in the world to examine it from every angle. You want them to look for design shortcuts, hidden weaknesses and unexpected combinations of parts that make the lock easier to open.

Traditionally, that testing has depended on highly specialised human expertise. It still does. But AI can potentially act as a force multiplier:

proposing hypotheses,

conducting repetitive exploration,

searching across a larger space of possible attacks and

helping researchers identify where their attention may be most valuable.

That may be good news for defenders. It also means that flaws can be detected earlier.

But it also means that the old model of cryptographic assurance - where a design survives a few years of expert review and therefore feels settled - may become less reassuring. AI is making it plausible that designs will face sharper scrutiny sooner.

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What HAWK actually tells us?

HAWK was designed as a post-quantum digital-signature scheme.

For example, signatures help verify that a software update really came from its developer. They help establish trust in firmware running inside devices. They can be used for identity, secure transactions and long-lived records.

The reported HAWK attack did not produce an instant practical attack against all post-quantum cryptography. It found a mathematical property that substantially reduced the estimated effort required to recover an equivalent secret key for relevant parameter sets. The practical implication was that restoring the intended security strength would require more conservative parameters, which could undermine HAWK’s performance and efficiency advantages.

This is an important lesson in how cryptography works in the real world.

A scheme can look secure enough at one size, fast enough at another and easy enough to implement at a third. Once a new attack is found, the balance may change. Increasing key sizes or other parameters can restore security, but it may also make a system slower, heavier or more difficult to deploy.

Cryptography is not only maths. It is engineering, economics, software design and long-term risk management.

AI has not broken today’s encryption

It is important not to overstate the story.

Similarly, the HAWK result does not mean ML-KEM, ML-DSA or all lattice-based cryptography is suddenly unsafe. HAWK was a separate candidate with its own design choices, and it has been removed from consideration before standardisation.

That distinction matters because irresponsible headlines can create the wrong kind of urgency. Organisations do need to prepare for post-quantum migration, but they should not rush into poorly understood changes because an AI-related claim appears on social media.

The better takeaway is this:

AI is not breaking deployed post-quantum standards today. It is making the process of finding and validating weaknesses faster.

That is a serious development, but it is not a reason for panic.

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There are now two clocks

Most organisations think about the quantum threat using one clock: when will a quantum computer be capable enough to break RSA or elliptic-curve cryptography?

That is the quantum clock. It matters because much of the internet and enterprise technology still relies on public-key cryptography that was not designed to withstand a large fault-tolerant quantum computer.

But there is now a second clock: the AI clock.

The quantum clock explains why organisations will eventually have to migrate.

The AI clock explains why they should not assume that the security assumptions made today will remain untested for years.

Together, those clocks point toward the same conclusion: build systems that can adapt.

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The hidden problem is not just TLS

When executives hear “quantum-safe encryption,” they often think about a website using HTTPS. That is only a small part of the challenge.

A company may be able to upgrade the cryptography at its public web edge relatively quickly, particularly when major cloud providers and content-delivery networks support hybrid post-quantum connections. But the harder problem is often inside the organisation.

Where are the signing keys used to approve software releases?

How are internal services authenticating one another?

What secures firmware updates to devices in the field?

What protects encrypted backups that may need to remain confidential for years?

Which identity systems, industrial gateways, VPNs, APIs, containers and machine-to-machine connections rely on older public-key algorithms?

Many organisations cannot answer those questions with confidence.

That is the real operational risk.

Start with a cryptographic inventory

The most useful response to this moment is not to immediately replace every encryption algorithm. It is to establish visibility.

A practical cryptographic inventory should identify:

• Where public-key cryptography is used across products, IT systems and operational technology.

• Which algorithms, key sizes, libraries and certificates are in use.

• Which systems rely on digital signatures, including code-signing, device identity and firmware updates.

• Which data must remain confidential for a long time.

• Which systems are difficult or expensive to update.

• Which suppliers and platforms already offer support for hybrid or post-quantum approaches.

This does not have to begin as a massive enterprise-wide project. A company can start with the systems where the consequences of failure would be greatest: customer data, software release pipelines, identity infrastructure, high-value intellectual property, industrial control systems and long-life connected devices.

For a startup, this can be even more important.

Crypto-agility is the real objective

The phrase “cryptographic agility” sounds technical, but the basic idea is straightforward.

A cryptographically agile system can change its security methods without requiring a complete redesign of the product, network or service.

That does not mean swapping algorithms casually. Cryptographic changes should be carefully tested and independently reviewed. But it does mean avoiding systems where one old algorithm is hard-coded into every component and cannot be replaced without disrupting the whole business.

For example, an organisation should be able to identify where certificates are issued, which libraries handle signatures, how keys are stored, and what must change when an algorithm needs to be retired.

This is where the HAWK story becomes useful beyond cryptography research.

A candidate scheme was challenged. A weakness was found. The candidate was withdrawn. That is what adaptation looks like at the standards level.

Organisations need the same ability at the deployment level: to change course before an avoidable technical dependency becomes a security crisis.

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Quantum computing sets the long-term deadline for migration. AI-assisted cryptanalysis may be bringing forward the moment when weak assumptions are exposed.

The encouraging part of HAWK story is that the system worked.

Researchers investigated a candidate. A weakness was identified. The designers and standards community acted before the scheme was standardized or broadly deployed. NIST’s selected standards remain in place, while the broader PQC process continues to examine additional options.

That is not a sign of chaos. It is the value of open scrutiny.

The uncomfortable part however, is that AI may make that scrutiny more intense. It may force cryptographers, standards bodies, vendors and security teams to operate at a faster pace. The bottleneck may increasingly be not just finding a weakness, but validating it, responsibly disclosing it, updating software and communicating clearly about real-world impact.

That will require more expertise, not less and a sensible response is neither complacency nor alarm.

Do not conclude that AI has broken post-quantum cryptography. It has not.

Do not assume that quantum risk can be ignored until a future breakthrough in quantum hardware. It cannot.

Instead, treat this as a prompt to do the essential work:

• Map where your organisation depends on cryptography.

• Identify long-lived data, software signatures and trust anchors.

• Ask vendors about post-quantum roadmaps and hybrid deployment options.

• Build systems that can support future algorithm changes.

• Make cryptographic agility a normal engineering requirement, rather than an emergency project.

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The United Kingdom has made a notable intervention in the global effort to commercialize quantum technology. In June, the government committed £10 million to create the Quantum Standards Network (QSN), a national initiative managed by the National Physical Laboratory (NPL), the UK’s national metrology institute. It brings together government, industry, research organisations and standards bodies to address how quantum technologies should be measured, tested, benchmarked and standardized.

Quantum computing, sensing, communications and their enabling hardware are progressing beyond the stage at which a laboratory result alone can carry a commercial proposition. A quantum system must not only achieve a technically impressive result, it must be possible for a customer to interpret its performance, compare it with alternatives, verify its claims and trust it in a real operating environment.

This is where metrology and standards enter the picture.

Metrology - the science of measurement - creates reliable ways to quantify a system.

Standards provide common definitions, test methods and reference points.

Together, they form the assurance layer between a promising demonstration and a product that can be qualified, procured, financed, integrated, insured and exported.

The QSN is therefore not merely technical housekeeping. It is an attempt to construct market infrastructure for a technology sector that is still defining its products, supply chains and buyer expectations. Yet it should be assessed with discipline. A network can coordinate people, map gaps, publish guidance and strengthen technical expertise. All of that is useful. None of it, however, automatically creates adoption.

A standards network does not by itself make a procurement team write a requirement into a tender. It does not automatically persuade an overseas customer to accept a UK test method. It does not guarantee that a quantum sensor will survive vibration, temperature shifts and years of field operation, or that a quantum computer will be assessed through benchmarks meaningful to a commercial user.

The real question is whether QSN turns coordination into lower risk and lower friction for the market.

To understand what is at stake, the initiative needs to be viewed through five connected layers: policy, industry, commercial adoption, research and international alignment.

Each captures a different stage in the journey from a government strategy to a technology that customers can trust and buy. Ignore any one of them, and the standards effort risks remaining valuable in principle but limited in market impact.

1. Policy layer

At the policy level, QSN signals that the UK is treating quantum standards as strategic infrastructure rather than a peripheral compliance issue. The network is managed by NPL and supported by a wider group that includes the Department for Science, Innovation and Technology, ( Now DBIST after a merger with DBT in July ) the British Standards Institution, the National Quantum Computing Centre, the National Cyber Security Centre and UKQuantum. Its intended role includes developing skills, helping businesses navigate standards and strengthening British influence in international standardisation.

That is a more mature view of technology policy than simply funding scientific research or backing a handful of flagship demonstrators. Research funding matters. Manufacturing capability matters. Venture investment matters. But standards often shape the conditions under which those investments become commercially usable.

They influence how performance is specified, how suppliers qualify parts, how systems are certified, how contracts are written and how products reach foreign markets. The countries that help define “what good looks like” in an emerging technology can gain influence before supply chains and technical architectures become settled.

The UK’s wider semiconductor-metrology strategy makes a similar case.

NPL’s roadmap argues that the country must treat measurement science and standardisation as integral to its semiconductor strategy, not as peripheral technical concerns. That is particularly relevant to quantum, where physical platforms, components and performance measures remain diverse.

The policy challenge is to avoid confusing activity with outcome. Success should not be counted in workshops held, reports published or committees created. It should be counted in the commercial effects of standards work:

shorter qualification cycles,

more credible procurement requirements,

lower buyer uncertainty,

more robust supply chains and greater export access.

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2. Industrial layer

Quantum technologies do not exist in isolation from the industrial hardware economy. Their future depends on a web of capabilities that includes specialised materials, photonics, RF electronics, control systems, cryogenic equipment, advanced packaging, interconnects, fabrication processes and testing infrastructure.

The NPL semiconductor roadmap is useful context because it shows how broad that industrial foundation is. It identifies a lifecycle of needs stretching from material-property measurement, material-quality metrics and defect classification to process inspection, trustworthy AI-enabled data flows, heterogeneous integration, package testing, hardware security, performance testing and device-reliability standards.

Quantum hardware intersects with almost all of those areas.

A quantum computer can demonstrate impressive processor performance, yet remain difficult to deploy if its control electronics are bulky, unstable or power hungry. A photonic system may promise scale, but be constrained by component loss, packaging complexity or inconsistent characterization methods. A quantum sensor can achieve extraordinary precision in a laboratory and still fail commercially if its calibration drifts, its operating envelope is unclear or its data cannot be integrated with conventional systems.

The point is not that quantum hardware should be treated as merely another semiconductor product. Different physical platforms will require specialised methods and different metrics. The point is that quantum’s commercial challenges increasingly resemble those of advanced hardware more broadly: repeatability, packaging, integration, reliability, quality assurance and manufacturability. ( more on manufacturing here)

This is where a national metrology institute can create leverage.

NPL’s role is not to choose winning quantum platforms. It is to help ensure that emerging components and systems can be measured and trusted well enough to compete and integrate with incumbent technologies.

For the UK, this is a more realistic strategic proposition than aspiring to dominate every volume-manufacturing segment. Britain is unlikely to rival Taiwan, South Korea or the United States in large-scale advanced silicon production. But it has recognized strengths in compound semiconductors, photonics, materials science, power electronics, device modeling, semiconductor tooling and applied measurement science.

Those strengths could be valuable precisely where markets are technically immature and the rules have yet to be written.

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3. Commercial layer

The commercial layer is where the argument becomes most concrete: standards matter only when they reduce friction.

That friction appears across the entire route to adoption.

A buyer needs a way to compare two suppliers.

A procurement team needs requirements that are clear without being overly prescriptive.

A system integrator needs to understand interfaces, operating limits and failure modes.

An insurer needs evidence on reliability and operational risk. A manufacturer needs qualification methods.

An exporter needs a way to demonstrate compliance in more than one market.

A useful standard helps each of these actors make decisions with more confidence.

Quantum sensing is likely to offer the clearest near-term test. The sector often communicates value through a headline performance metric: sensitivity to magnetic fields, acceleration, gravity, time or another physical quantity. This is scientifically meaningful, but it is not yet a deployment case.

A transport operator considering quantum navigation in a GNSS-denied environment will ask different questions. What is the sensor’s size, weight and power draw? How does it perform under vibration? How frequently must it be calibrated? How much does it drift over time? How does it work alongside inertial systems, conventional sensors and software? What happens when different instruments disagree?

An industrial customer evaluating quantum sensing for condition monitoring will want to know about false alarms, environmental tolerance, uptime, integration cost, maintainability and lifecycle economics. An exceptional laboratory result does not answer these questions.

The QSN’s remit includes common requirements for quantum technologies, including quantum-sensor considerations such as size, weight, energy efficiency and trusted readings between instruments. This is exactly the direction standards work must take if it is to be commercially relevant.

The same principle will matter in quantum computing. The market cannot mature indefinitely through vendor-specific metrics that are difficult for customers to reconcile. Different hardware architectures should not be forced into a false uniformity. But users will need more transparent approaches to benchmarking, reliability, security, workload suitability, availability and integration costs.

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4. Research layer

The research layer is equally important because the performance metrics that dominate early-stage quantum science are not always the metrics needed for industrial use.

Academic research should continue to reward discoveries, novel physical effects and performance records. Those achievements are the source of quantum technology’s long-term potential. Yet a breakthrough demonstration does not necessarily establish repeatability, maintainability, manufacturability or reliability in a deployed system.

Those questions demand another kind of work: calibration, long-duration testing, failure analysis, environmental characterisation, uncertainty quantification, benchmarking and measurement under realistic conditions. It is less glamorous than a new record. It can be just as decisive for adoption.

The semiconductor roadmap makes this explicitly.

It argues that academia needs to align fundamental research more closely with the measurement and qualification needs that shape industrial adoption. This should not be interpreted as a demand that all research become short-term product development. It is a call to recognise measurement science as a high-value bridge between discovery and deployment.

The QSN could help create that bridge by identifying pre-competitive problems that no individual start-up has the incentive or resources to solve alone. A company may be able to optimize its own device, for example, but it may not be able to establish broadly accepted reference methods, reliability tests or calibration protocols across an entire technology category.

Shared standards work can create this public-good infrastructure. But it needs to be informed by real industrial needs. Researchers must know what information customers lack. Industry must engage before standards become fixed. And end-users must define what evidence would allow them to deploy, not merely trial, the technology.

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5. International layer

The final layer is international relevance.

Quantum is an inherently global field:

That means a national standards network is useful only to the degree that it helps build cross-border acceptance.

NPL already supports UK participation in international and regional standards bodies including ISO, IEC, ITU-T, ETSI, CEN and CENELEC. Its quantum programme also develops methodologies for characterizing quantum technologies, an essential contribution to standards development. This creates a credible starting point for the QSN.

But attendance in international forums is not enough. The UK needs to bring strong technical evidence, coordinated industry positions and methods that other countries have a reason to use. International standards emerge through a combination of measurement credibility, sustained participation, industrial adoption and commercial power.

Here, the UK should think beyond a domestic rulebook.

The goal is not to create “British standards” that fragment an already complex market. It is to ensure that UK-developed methods and priorities make meaningful contributions to standards that are internationally recognised.

This is the difference between a network that convenes national expertise and one that influences global market access. If the QSN helps British companies shape common methods early, it may prevent them from simply adapting to rules defined elsewhere. If it makes UK testing and measurement practices legible abroad, it can reduce barriers for firms entering international supply chains.

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From coordination to consequence

First, it should identify a limited number of quantum measurement and assurance problems where uncertainty is already delaying deployment. Quantum-sensor calibration and drift, environmental testing, photonic-component characterisation, quantum-control electronics, reliability, cybersecurity and procurement-relevant benchmarks are all plausible priorities.

Second, it should place end-users at the centre of the process.

Standards developed solely by researchers and suppliers may be technically sophisticated but commercially peripheral. Infrastructure owners, aerospace firms, transport operators, manufacturers, financial institutions, healthcare organisations, public-sector buyers and insurers understand the evidence required before they will accept deployment risk.

Third, it must create routes into commercial mechanisms.

A standards initiative becomes meaningful when its outputs are used by a test house, cited in a tender, embedded in a certification process, adopted in a supplier-qualification protocol or incorporated into a customer acceptance test.

Finally, it should make international adoption a hard success metric.

The question is not whether the UK can convene an impressive national network. It is whether British firms will be able to use the resulting work to build, qualify and sell products in international markets.

The UK is right to recognize that quantum advantage cannot be measured only in qubits, sensitivity records or laboratory milestones. It will also be measured in whether technologies can be independently tested, responsibly specified, confidently purchased and reliably deployed.

The Quantum Standards Network is an important attempt to build that missing layer of market infrastructure. But a network is not yet evidence of standards adoption or commercial impact.

That evidence will come when quantum suppliers qualify products faster, when buyers compare offerings with confidence, when standards appear in procurement and assurance processes, and when UK-developed methods are recognized beyond Britain. Only then will the initiative have moved from coordination to consequence.

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