As generative AI pushes the global computing race to new heights, the industry's next question is no longer just how large AI models can grow, but how the underlying computing architecture will evolve to sustain them. At AI EXPO Taiwan 2026, representatives from Google Quantum AI, NVIDIA Quantum Computing, and Taiwan's academic community converged on a shared assessment: large-scale commercial quantum computing is still years away, but AI has already become a critical accelerator helping quantum technology move from the laboratory toward next-generation supercomputing architecture.
That assessment helps explain why major technology companies continue to pour resources into quantum, even as the field has yet to reach commercial maturity. In its latest quantum roadmap, IBM designated 2026 as a significant validation milestone, with a stated goal of demonstrating the first case of "scientific quantum advantage" within a hybrid quantum-HPC architecture. IBM also stressed that these are current targets and plans, not accomplished facts — signaling that 2026 is better understood as a checkpoint for proving quantum advantage than a threshold into full commercialization.
The Hardest Hardware Problem: Noise, Stability, and Error Correction
At the forum, Ping Yeh (葉平), software engineering manager at Google Quantum AI, offered a pointed reality check. If the audience remembered only one thing, he said, it should be this: "Quantum computers today cannot solve any truly valuable real-world problems." In his framing, quantum computing remains closer to a research instrument than a production system that enterprises can deploy at scale. The barrier is not a lack of imagination — it is that any commercially valuable quantum application ultimately depends on reliable hardware, and the core obstacles of noise, stability, and error correction have not yet been overcome.
Yeh's remarks served to address two common misconceptions. The first is that quantum will imminently replace GPUs and CPUs. The second is that simply scaling up qubit counts will naturally deliver commercial viability. His view is more grounded: while academic discussions of quantum advantage often emphasize dramatic speedups over classical algorithms, commercial applicability does not require that level of contrast. If a quantum system can solve a certain class of problems even slightly faster and at slightly lower cost, that may already constitute a real-world business opportunity.
The gap between "theoretically feasible" and "practically deployable" remains wide. Yeh broke down quantum application development into multiple stages: starting with abstract algorithms, identifying which problems can genuinely benefit from them, translating laboratory problem setups into real commercial needs, and then continually reducing demands on qubit counts and computational resources. The challenge, he noted, is rarely a shortage of imaginative use cases. It is whether real-world problems actually fit the conditions under which a given algorithm works, and whether resource costs can be pushed low enough for practical adoption.
AI Has Become the Foundational Tool Driving Quantum Forward
While Google offered a reality check on the present, NVIDIA Quantum Computing Developer Relations manager Linsey Rodenbach sketched out the technical path ahead. The central concept in her presentation was not the standalone quantum computer, but what she described as the "accelerated quantum supercomputer."
In her vision, the systems that will ultimately deliver value will not be isolated quantum machines accessed remotely through the cloud, but hybrid supercomputing architectures that integrate quantum hardware, AI, classical infrastructure, networking, and HPC directly within a single data center.
This perspective shifts the central question from "will quantum replace existing computing?" to "how will quantum be co-built with existing computing?" Rodenbach was direct: the quantum systems of the future will not be "two qubits working alone in a corner," but an integrated system of "qubits plus AI plus classical infrastructure plus networking." Within that architecture, AI is not a peripheral theme — it is the foundational tool enabling quantum to advance, supporting control, calibration, compilation, and error correction.
Rodenbach also declined to treat "Quantum for AI" and "AI for Quantum" as separate tracks. She argued that if quantum computers are understood as a new kind of scientific instrument, one of their near-term contributions will be generating high-fidelity data for physics, chemistry, and biology — data that can then be fed into existing AI models to improve them. AI, in turn, can generate synthetic data, optimize hardware efficiency, and help connect larger quantum system networks. She stressed this is not a distant prospect: in certain representative problem domains, AI has already accelerated research workflows by hundreds or even thousands of times, making previously infeasible work tractable.
Taiwan's Advantage Lies in Its Semiconductor Supply Chain
Taken together, the positions of IBM, Google, and NVIDIA sketch out a coherent industry map. IBM is describing a target — 2026 as a key validation node for quantum advantage. Google is describing the present — quantum computers are still far from solving high-value real-world problems. NVIDIA is describing the path — quantum will reach commercial relevance not through a standalone hardware breakthrough, but by being integrated with AI, HPC, and existing data center infrastructure into a new computing architecture. The three perspectives appear to approach the question from different angles, but together they form a single, coherent map of where the industry stands.
Where Taiwan fits within that map was the central question raised by Chang Ching-ray (張慶瑞), director of the Quantum Information Center at Chung Yuan Christian University. In his view, the more important near-term question is not whether Taiwan can quickly produce a full-stack quantum computer manufacturer, but what role Taiwan can play once quantum transitions from research to productization and supply chains begin to form. Chang argued that as major U.S. companies gradually release specifications, Taiwan's comprehensive semiconductor supply chain gives it a genuine opportunity to secure a position in the emerging quantum supply chain.
He was careful, however, to note that the quantum supply chain is not simply a copy of the conventional semiconductor supply chain. If traditional computers resemble automobiles, he said, quantum computers are more like aircraft — both require components, materials, testing, and integration capabilities, but the specifications and system requirements are fundamentally different. Taiwan cannot expect its existing semiconductor expertise to transfer automatically; the viable approach is to reposition existing strengths in chips, modules, systems, and manufacturing within the division of labor that the quantum supply chain will require.
Not "Quantum Replaces Everything Tomorrow"
Chang's advice to Taiwanese companies was pragmatic: firms without existing quantum foundations should not attempt to build comprehensive capabilities from scratch, but instead look for entry points through established international partnerships. The practical question, he said, is not "should I build my own quantum computer?" but "what can I do for the partners I already work with?" From that perspective, quantum is not a race that breaks entirely from the semiconductor industry — it looks more like a reorganization of the division of labor among AI, semiconductors, and next-generation computing architectures.
The real meaning of "AI plus quantum" is not that quantum will replace everything tomorrow. It is that the next round of the computing race has already moved beyond the competition of individual chips and individual models, toward a contest over ecosystems, data center architecture, and supply chain integration. Quantum has not yet reached mass commercial deployment, but AI is actively shortening the distance. For Taiwan, the critical question is not who tells the most compelling quantum story, but who secures a position in the future quantum supply chain before specifications are fully set. That race will not be decided tomorrow — but the positioning has already begun. （Related： Opinion | Financial Warfare: Why Japan Might Short The Global Oil Market ｜ Latest ）
