Quantum themes are now everywhere—from multiverse plots to Schrödinger’s cat, the famous paradox of a cat that is both alive and dead until observed. This surge in storytelling isn’t just cultural curiosity. Experts see it as a sign that imagination is beginning to meet scientific feasibility.

When Sci-Fi becomes signal

The trajectory echoes that of artificial intelligence. AI appeared in fiction for years, but only reshaped industries after deep learning breakthroughs in 2012. Quantum computing may be approaching a similar tipping point.

Until recently, quantum ideas lived mostly in theory. But researchers are now building real systems—stabilising qubits and testing early quantum algorithms.

As science catches up to storylines, analysts note that public imagination often signals where real progress is emerging. For those watching closely, quantum’s rise in fiction could be more than a coincidence—it could be the earliest sign of real-world transformation.

A new kind of computation

Quantum computers differ fundamentally from classical machines. While traditional systems handle tasks step by step, quantum computers explore many possibilities at once—thanks to quantum phenomena like superposition and entanglement.

One expert likens it to a scene from Avengers, where Doctor Strange scans millions of futures simultaneously to find the best path forward. That’s essentially how quantum systems approach complex problems—by evaluating countless outcomes in parallel.

This makes them especially suited for challenges that overwhelm classical systems, such as:

Cracking next-generation encryption
Optimising vast logistics networks
Simulating molecular interactions in drug or material discovery

Quantum isn’t here to replace everyday computing. But for specific high-complexity problems, it represents a fundamentally new—and more powerful—computational model.

Where quantum will hit first

Quantum computing is expected to make its earliest impact in industries where computational complexity is high and financial upside is significant.

Key early applications include:

Pharmaceuticals: simulating molecular interactions to accelerate drug discovery
Advanced materials: designing new compounds or batteries at the atomic level
Finance: optimising asset portfolios, particularly in ETFs and derivatives

Take ETF construction, for example. Selecting the ideal combination of dozens of assets involves combinatorial optimisation—a task that becomes exponentially harder as the number of variables increases. While AI tools help, classical systems struggle beyond a point. Quantum computers, by evaluating multiple combinations simultaneously, offer a clear advantage.

Also Read: Navigating Asia’s business boom: The quantum leadership advantage

In the short term, industries that combine high complexity with high value potential are best positioned to adopt quantum solutions—because the benefits justify the infrastructure investment.

Early wins in the quantum race

While today’s quantum computers remain in the early stages—most with only a few dozen usable qubits—they are already beginning to show practical value in select domains. Many available qubits are still dedicated to error correction, reflecting the sensitivity of current hardware.

Yet despite these limits, meaningful use cases are emerging.

Notable early applications include:

Drug discovery: simulating molecular behaviour at quantum levels
Advanced materials: modelling atomic interactions for next-gen compounds
Finance: improving asset rebalancing strategies in complex portfolios
Logistics: optimising large-scale routing problems that scale exponentially

These are all areas where classical systems struggle as complexity increases.
Even with today’s constraints, quantum systems are starting to outperform traditional methods in narrow but high-impact scenarios.

The technology may still be maturing, but its real-world value is no longer theoretical—it’s beginning to take shape.

Why quantum-AI hybrids matter

Quantum–AI hybrid computing is drawing growing attention as a practical way to extract early value from quantum systems. Rather than replacing classical computing, the hybrid model assigns different parts of a task to the most suitable processor.

Classical computers or AI handle large-scale, repetitive calculations
Quantum systems tackle tasks involving simulation, optimisation, or quantum-specific modelling
Cloud platforms or machine learning layers integrate and interpret the combined outputs

This division of labour leverages the strengths of each architecture, delivering faster, more efficient results than either could alone. Experts see hybrid models as the most viable short-term strategy—not only technically, but also commercially and operationally—to scale quantum’s impact without waiting for perfect hardware.

Rethinking the internet for a quantum era

If quantum computers reach commercial viability, today’s internet security architecture will require a complete overhaul. Most current encryption methods—used in banking, e-commerce, communications, and authentication—are vulnerable to quantum algorithms capable of breaking them. This wouldn’t just call for software updates; it would demand a structural redesign of global digital infrastructure.

Experts describe it as a foundational shift, not a technical patch. That said, the transition is expected to unfold gradually over the next decade, giving rise to quantum-resistant cryptographic standards and long-term planning by governments and enterprises.

For infrastructure providers and investors, the key is timing: anticipating when and how to adapt before disruption becomes inevitable.

Korea’s quantum edge: Beyond hardware

While Korea has produced notable quantum researchers, including one of IonQ’s co-founders, full-scale hardware development remains concentrated in global hubs like the US, where companies such as IBM, Google, and IonQ lead in capital and infrastructure.

Instead, Korea is gaining ground in quantum-resilient infrastructure, particularly in quantum-safe cybersecurity. A standout example is SK Telecom, which acquired Swiss-based ID Quantique—a global leader in quantum key distribution (QKD)—and later entered a strategic partnership with IonQ.

Also Read: Horizon Quantum CEO on the Singapore advantage in starting a quantum computing company

This positions Korea to lead in quantum-proof security systems, a field likely to reach commercial scale well before universal quantum computing becomes mainstream.

Experts draw parallels to the early AI wave (circa 2014–2015), when Korea didn’t build foundational frameworks but found success through application-level innovation. Similarly, Korea’s future in quantum may lie in industry-specific algorithms, secure infrastructure, and applied software—not hardware.

How big tech is positioning for quantum leadership

Major tech companies are taking two main paths toward quantum computing: in-house development and strategic partnerships or acquisitions.

IBM and Google have pursued full-stack integration from the outset—developing quantum hardware, software tools, and embedding quantum capabilities directly into their cloud platforms. They remain the most vertically integrated players in the field.
Microsoft and Amazon initially focused on enabling quantum access through their cloud ecosystems, partnering with startups to provide tools via Azure and AWS. But as the commercial potential grows, both are moving toward greater internal control.

Recent shifts include:

Microsoft’s launch of proprietary tools through initiatives like MyOrionow
Reports of Amazon collaborating with or acquiring startups such as OxenT to build its own quantum stack

This signals a broader trend: big tech is transitioning from collaboration to ownership, aiming to secure key positions as quantum computing moves from theory to viable markets.

Quantum investing: Echoes of the deep learning era

Some early-stage investors see clear parallels between today’s quantum computing landscape and the deep learning boom of the mid-2010s.

In 2012, deep learning began to show real promise. By 2013–2014, major tech firms were investing heavily. During that wave, investors backed AI startups that later went public after a 7-year growth cycle.

Quantum computing now appears to be following a similar arc:

Foundational research is maturing
Big tech is entering aggressively
Use cases are emerging in finance, pharma, and cybersecurity

Many investors haven’t placed direct bets yet, but they’re watching closely.
Angel investors may need a 10-year horizon, while VCs could see returns within three to five years years.

The consensus among early movers? This is the beginning of a new wave—and the time to position is now.

The quantum era won’t arrive overnight—but once it’s here, it will move fast. Those who act early won’t just adapt to the future—they’ll help shape it.

Special thanks to Dr. Jihoon Jeong, General Partner of Asia2G Capital, for his valuable contributions to this article.

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