Quantinuum, one of the world’s leading quantum computing companies, has already put a number on it: a trillion-dollar market waiting to be unlocked once fault-tolerant quantum computing arrives. That’s not a typo, and it’s not a crypto-style promise built on speculation — it’s an estimate built on real, quantifiable industries: drug discovery, materials science, chemicals, energy and finance, where a single better answer can be worth billions on its own. The uncomfortable question for founders, investors and policymakers in Asia is the same one many asked too late about AI and too late (or too early, and badly) about crypto: will you be building the picks and shovels for this boom, or reading about it after the fact?

Unlike the crypto cycle, this isn’t hype chasing a use case. And unlike the early days of AI, the economic bar for quantum computing is brutally explicit: DARPA and industry alike are converging on one test — a quantum calculation only counts commercially once it demonstrably saves more money than it costs to run. A pharmaceutical company that spends billions developing a single drug, for instance, could justify an extraordinarily expensive quantum calculation if it meaningfully cuts the odds of a failed candidate. That is the bar quantum computing has to clear before it can have its own “ChatGPT moment” — and it’s a far harsher bar than the one artificial intelligence had to clear.

Quantum computing is no longer purely theoretical. The harder question is whether it can become economically useful. For Asia, the opportunity may be less about owning quantum computers and more about building the industries, infrastructure and applications around them.

Why quantum computing’s commercial breakthrough won’t look like AI’s

Artificial intelligence had been around for decades before ChatGPT made it commercially legible. Neural networks existed. GPUs existed. Large language models existed. What changed was that several prerequisites converged at roughly the same time: sufficient computing power, enormous datasets, scalable cloud infrastructure, better algorithms, usable interfaces, and a business model that let millions of people access the technology without understanding the underlying mathematics.

Quantum computing may be approaching its own equivalent transition. But there’s an important distinction. AI became useful while remaining imperfect. Quantum computing must overcome a much harsher economic constraint: its output must be valuable enough to justify an extraordinarily expensive physical machine.

DARPA has put the question unusually clearly. Its Quantum Benchmarking Initiative is trying to determine whether a quantum computer can achieve “utility-scale” operation by 2033 — meaning its computational value exceeds its cost. That may ultimately be the only quantum benchmark that matters.

Quantum today looks a little like AI before the commercial explosion

There’s a useful, if imperfect, analogy. AI progressed roughly like this:

Academic research → specialist systems → cloud APIs → foundation models → consumer applications → enterprise infrastructure

Quantum computing could follow a parallel but distinct path:

Physics experiments → error-corrected logical qubits → specialist scientific applications → cloud-accessible accelerators → industry workflows → national computational infrastructure

The difference is that AI could run on increasingly commoditised silicon. Quantum computing requires extremely specialised infrastructure: superconducting systems may need temperatures close to absolute zero; trapped-ion machines need lasers and ultra-high vacuum systems; neutral-atom machines require sophisticated optical control. The result is that the quantum revolution is unlikely to put a quantum computer on everyone’s desk.

Also Read: AI, quantum computing and the future of cyber threats

Instead, picture a future cloud architecture where a business application sits above an AI orchestration layer, which in turn routes work across CPUs, GPUs and QPUs — CPUs for business logic, GPUs for AI models, and QPUs for quantum chemistry or optimisation — before producing a single business output. Most users may eventually consume quantum computing without ever knowing a quantum processor was involved. That’s probably the real commercial end state.

The quantum roadmap: From laboratory to invisible infrastructure

Any timeline remains speculative, but current company roadmaps provide useful boundaries. IBM’s current hardware roadmap targets its Starling fault-tolerant system for 2029, with 200 logical qubits and 100 million quantum gates. Quantinuum’s published roadmap targets universal, fully fault-tolerant quantum computing by the end of the decade. These are company targets, not guarantees.

DARPA provides a useful external counterweight to vendor roadmaps: its 2033 utility-scale test is explicitly designed to separate industrial usefulness from hype. On the hardware side, Google’s Willow chip demonstrated “below-threshold” error correction in late 2024 — a long-sought milestone where adding more physical qubits makes a logical qubit more reliable rather than less, which is itself a prerequisite for any of these roadmaps to hold.

Five prerequisites have to converge before quantum computing pays off

• Fault tolerance must become economically practical. Physical qubits are noisy. Useful machines need logical qubits created using error correction. The commercial question isn’t whether error correction works in principle, but how much physical infrastructure, energy, control hardware and runtime are required to produce one reliable logical computation.
• Useful algorithms need to appear. Having a quantum processor without useful algorithms is analogous to having GPUs without deep learning. The most commercially important breakthrough may come not from hardware but from discovering algorithms that transform high-value industries.
• Classical computing has to lose somewhere. Quantum doesn’t compete against computers from 2020. It competes against whatever GPUs, supercomputers, AI models and optimisation software exist when fault-tolerant quantum systems arrive. The baseline is a moving target.
• Developers need abstraction. Most software engineers will never design quantum circuits. Commercial adoption requires APIs, orchestration layers and hybrid workflows that route only the structurally suitable part of a problem to the QPU.
• The economics must close. The final equation is simple: the economic value of the quantum result must exceed the cost of the QPU, HPC, energy, talent, integration, sampling and error correction. Quantum can be scientifically revolutionary while still being commercially irrational.

In practice, a hybrid workflow is emerging: AI decomposes a problem, classical HPC narrows the candidates, a QPU tackles the hard quantum subproblem, AI interprets the output, and a human validates the result before it’s acted on.

Also Read: The AI-quantum collision: Navigating the 2026 infrastructure inflection point

Where the money may actually be in quantum computing

The strongest early applications for quantum computing use cases share one characteristic: a single computational answer can be worth enormous amounts of money.

For the Philippines example, see DOST-PCIEERD’s ongoing STArQE quantum algorithms project for EV and renewable-energy planning. DOST-PCIEERD’s 2026 programme update also frames quantum technology as a national capability-building priority for future applications in energy, health, defence and information technology.

Who is leading the global quantum computing race?

There’s no universally accepted national ranking, because countries lead in different layers of the stack. The broad competitive map looks like this:

Singapore’s commitment is unusually concrete for Southeast Asia. The Singapore National Quantum Strategy has close to SG$300 million (US$233 million) set aside under RIE2025 for quantum research, talent and coordinated capability building.

What does a quantum-enabled Singapore actually look like?

Singapore’s geography makes it an unusually good candidate for early application. It’s small, urbanised, capital-rich, highly networked and concentrated around sophisticated sectors.

A bank doesn’t need to own a quantum computer. Its risk platform could send an unusually hard computational subproblem to a regional quantum service, sitting on top of a national HPC and quantum cloud. A pharmaceutical laboratory could use AI to generate molecules, conventional computing to eliminate obvious failures, and quantum processing for the subset where classical modelling becomes prohibitive. A port system could combine AI forecasting, classical optimisation and quantum algorithms only for hard search spaces where there’s a demonstrated advantage.

Also Read: Quantum computing’s double-edged sword could threaten cybersecurity: Report

Singapore’s advantage would be density: finance, biotech, government, universities, data centres and advanced infrastructure exist within one compact geography. Its national play may therefore be to become the application, finance, governance and integration layer for quantum computing in Southeast Asia, rather than to manufacture every layer of the stack itself.

The Philippines needs a very different quantum computing strategy

The Philippines should probably not copy Singapore. Its competitive advantage — and problem set — is almost the opposite: a geographically fragmented archipelago of more than 7,600 islands, uneven infrastructure, and large national-scale coordination problems across energy, logistics, and climate and disaster response.

Consider energy. The Philippines must coordinate island grids, renewable generation, storage, transmission limits, typhoon exposure, fuel imports, electricity demand and future EV infrastructure. AI can forecast demand, weather and failures. Classical optimisation can solve most operational decisions. A quantum service should be invoked only when a specific combinatorial or physical problem demonstrably exceeds the economic performance of classical computing.

The country is already building capability rather than waiting for mature hardware. DOST-PCIEERD has identified quantum technology as a national R&D priority and is currently funding projects including quantum-based forecasting and optimisation for the electric power grid.

The AI lesson quantum computing cannot afford to ignore

AI created enormous value. It also gave us benchmark inflation, hallucination, opaque models, exaggerated capabilities, concentration of compute and deployment ahead of governance. Quantum has an opportunity to avoid some of this by being more precise about what “advantage” actually means.

Only the fourth consistently creates a sustainable commercial market. A machine that performs a specialised calculation one million times faster but costs one billion times more has produced an impressive experiment, not an economic revolution.

Also Read: Quantum’s inflection point: Why the smart money is watching now

What to actually watch over the next decade

The most important quantum headline will probably not be “Company X reaches one million qubits.” A more consequential headline would be: “A pharmaceutical company identifies a commercially successful molecule using a quantum calculation that could not economically have been performed classically.”

At that point, investment follows. Software ecosystems emerge. Cloud providers commoditise access. Consultancies build implementation practices. Universities produce specialists. Startups stop selling “quantum” and start selling better batteries, new medicines, cheaper chemicals and lower financial risk.

The commercial endgame for quantum computing

The likely end state is a division of labour between human, AI, CPU, GPU and QPU — each solving the class of problem for which it has a structural advantage. AI determines what to investigate. Classical computing handles the overwhelming majority of computation. Quantum processors tackle narrow problems where quantum mechanics provides an economically meaningful advantage. Humans determine whether the answer should be acted upon.

For Singapore, that could create a new regional infrastructure and services industry. For the Philippines, it could offer access to computational capabilities that would be prohibitively expensive to build domestically.

The commercial quantum era begins not when the machines become powerful. It begins when someone can prove that not using one costs more.

Author’s note: Company roadmaps are forward-looking statements and are presented as targets, not independently guaranteed outcomes. The commercial timeline in this article is an analytical scenario based on current roadmaps and public evidence, not a prediction of certainty.

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