Quantum computing has long been treated as the ultimate moonshot: fascinating but comfortably far away from today’s P&L.
Then came Budget 2026’s challenge of that assumption. Hosting Quantinuum’s most powerful system outside US and boosting research and innovation funding to $37 billion through 2030 means that quantum is no longer just a physics story in Singapore. It is now part of the country’s broader economic strategy, alongside semiconductors, advanced manufacturing and artificial intelligence (AI).
The important question many business leaders are asking is not “How many qubits does my business need?” but “Why should I invest now, when I am still trying to make AI pay off?”. The answer lies in reflection. If AI transformation has taught Southeast Asia anything, it is that waiting until a technology feels ‘fully proven’ is exactly how you end up stuck in pilot purgatory.
However, there is a different opportunity with quantum. Think about Singapore as a global financial hub. A bank that runs thousands of market scenarios every day from price risk to stress‑test portfolios is already pushing the limits of what today’s optimisation tools can handle efficiently. As models, data and regulatory expectations grow more complex, the gap between “good enough” and “optimal” can translate into real money.
Embracing it now offers a chance to approach the next wave of quantum more deliberately, so that when the economics makes sense, you are ready to capture value rather than starting from zero. The organisations that start experimenting in a focused and low-risk way, will have the skills, intuition and use-case playbook ready when quantum technology scales further.
Quantum is closer than you think
One of the main misconceptions is that quantum doesn’t matter until you install a machine in your own data centre. For almost every organisation in Singapore and the region, quantum computing will arrive the same way as AI and high-performance computing -- through the cloud.
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It is a no‑brainer: these machines are expensive and scarce, and their capabilities evolve quickly, much like the most advanced GPUs. The cloud is the practical way to access the right hardware at the right time, without betting on a specific machine that could become outdated within a few years.
Quantum as a Service (QaaS) and access to quantum processing units (QPUs) are already enabling teams to move beyond experimentation toward targeted industrial use cases. Rather than abstract demonstrations, early value is emerging on constrained optimisation problems that are already difficult for classical heuristics.
Take a regional logistics firm trying to optimise thousands of delivery routes and warehouse slots across several cities. Classic solutions such as open‑source optimisation tools can get close to an answer, but as the network grows more complex, the cost and time required to find better solutions rise sharply. With Quantum‑as‑a‑Service, the logistics firm can model the problem on a quantum emulator in the cloud, then send selected scenarios to a real QPU to see whether a quantum‑enabled approach can find more efficient schedules or reduce empty miles.
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The goal is not to throw away existing optimisation software, but to add another tool for the hardest parts of the problem – where small improvements in efficiency can translate into meaningful savings or better service levels.
This approach keeps experimentation affordable and contained. There is no single “winning” hardware approach today. Hardware technologies have different strengths, trade‑offs and maturity levels. In a fast-changing field, this flexibility manages risk while keeping algorithms and expertise adaptable as hardware evolves and diversifies. It also avoids locking your quantum plans to one provider or tech that might not suit your key problems. For Singapore’s businesses and public agencies, that may be the most practical way to move from curiosity to capability.
A practical roadmap for the intrigued
Once you accept that quantum will arrive through the cloud, the question becomes: how do you engage without losing focus or wasting money? A few pragmatic steps can help.
- Pick the right problems
Focus on 2–3 stubborn, high‑complexity issues where classical methods are already straining (for example, logistics, risk, complex scheduling, simulation). When you evaluate the problems to experiment with, if the current approach is cheap and “good enough”, quantum can wait. But if it isn’t, that is a prime candidate for experimentation.
- Set up a lean, cross‑functional team and learn in the cloud
Set up a small, multidisciplinary and effective R&D team, or have a company from the ecosystem develop algorithms to explore quantum. Have them start on emulators to test ideas cheaply in a secure environment, and move to time‑billed QPU runs only when there is a clear hypothesis that a quantum approach could beat existing methods.
In the early stages, the most valuable outcomes are people who understand that quantum concepts can work with the tools, and can have serious conversations with partners and regulators.
- Design for flexibility, not one‑off pilots
Use platforms that support multiple quantum technologies and standard interfaces so you avoid early lock-in and benefit from hardware advances. Measure success not just in short‑term results, but in internal capability and clear plans for how promising experiments could be integrated into real systems and processes. Hybrid computing, broken down into several parts that can run on GPU and QPU, is one of the great possibilities of intermediate results before the advent of quantum supremacy computing.
By considering these pragmatic questions, businesses can work to avoid the “experimentation trap” that many have found themselves in when it comes to implementing AI. According to a joint survey between McKinsey and Singapore’s Economic Development Board, many Southeast Asian companies have moved beyond pilots, yet a large share remain stuck in what has been called the “experimentation trap” because isolated AI projects are never quite applied to alter the workflows where revenue and productivity are actually generated. The technology is there, but the core business looks much the same.
When it comes to quantum, running a single impressive proof‑of‑concept might be good for a press release but if it never leaves the lab, the business impact rounds down to zero. The lesson from AI is not “do not experiment”; it is experimenting with the intent to execute. So with each promising result, it is imperative to ask “what would we change in our processes, systems and governance if we scaled this?”
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Preparing for a quantum-enabled region
Singapore’s quantum push sits within a broader pattern: disciplined, focused investment in areas where the country can shape parts of global value chains, rather than compete on scale alone. Quantum is simply the next chapter in that story.
For the wider Asia‑Pacific region, this offers both a challenge and an opportunity: to avoid seeing quantum as “someone else’s future” until it is too late to catch up, and using the cloud-based tools already available to build skills and use cases at a pace that matches the region’s needs.
Quantum will not replace classical IT or AI. It will sit alongside them as another way to tackle a specific class of highly complex problems. The leaders who benefit most are unlikely to be those who chase the flashiest demonstrations, but those who proactively prepare their cloud, data and talent foundations so that when quantum techniques are ready for their sector, they too, are ready.
Fanny Bouton is the quantum and open innovation lead at OVHcloud
