What Emerging Quantum Computing Can Learn from the Growth of Semiconductors
Introduction: Lessons From My Experience in Semiconductors
When I began my career in Japan’s semiconductor industry in the early 1990s, I stepped into a world undergoing rapid transformation. The semiconductor boom of the 1980s and early 1990s had already set the stage for the modern computing revolution, and I had the privilege of witnessing firsthand how this industry scaled, standardized, and commercialized to become what it is today.
Now, over 30 years later, I see striking parallels between the early days of semiconductors and today’s quantum computing industry. Both technologies started in laboratories, both faced skepticism over their commercial viability, and both required breakthroughs in scalability and standardization before they could change the world.
The semiconductor industry’s journey—from niche to a $500 billion global powerhouse—offers valuable lessons for quantum computing. If quantum leaders can recognize and apply these lessons, they can accelerate quantum’s trajectory from research curiosity to an indispensable business tool.
Lesson 1: Standardization is the Foundation of Industry Growth
In the early decades of semiconductor development, from the 1950s through the 1970s, chipmakers each had proprietary designs, fabrication methods, and architectures. This lack of standardization meant that integrating chips from different manufacturers was nearly impossible, slowing industry adoption.
What I Learned in the 1990s
By the time I entered the semiconductor market, the industry had already begun to align around standards—notably the dominance of x86 and RISC architectures. These standards weren’t just technical conveniences; they were the key to mass production, cost reduction, and software ecosystem growth.
What Quantum Needs to Learn
Today, quantum computing is where semiconductors were pre-1980s—with multiple competing architectures (superconducting qubits, trapped ions, photonic qubits) and no universally accepted standard.
From my experience, industry-wide adoption only happens when businesses feel confident in interoperability and future stability. Quantum computing must follow the semiconductor industry’s path by:
✔ Establishing standardized quantum processor architectures (like x86 did for semiconductors).
✔ Creating universal quantum programming frameworks (just as we saw with C, then Java, then Python in the classical world).
✔ Aligning on error correction models so businesses don’t have to gamble on proprietary, unproven approaches.
The quantum companies that embrace standardization will be the ones that win the market.
Lesson 2: Scaling Requires a Breakthrough, Like CMOS Did for Semiconductors
In the 1960s and 1970s, semiconductor scaling was limited by high power consumption and unreliable transistors. The turning point came with CMOS (complementary metal-oxide-semiconductor) technology, which drastically reduced power consumption and improved stability. This innovation enabled mass production and laid the foundation for the microprocessor revolution.
What I Witnessed in the 1990s and 2000s
In the 1990s, I watched how semiconductor firms leveraged miniaturization and lithography advancements to make chips cheaper, faster, and more power-efficient. By the 2000s, the industry had mastered sub-100nm transistor manufacturing, driving the explosion of personal computing, mobile devices, and eventually AI and cloud computing.
What Quantum Needs to Learn
Quantum computing desperately needs its equivalent of CMOS. Right now, quantum processors:
Require extreme cooling to near absolute zero.
Have high error rates and short coherence times (unstable qubits).
Operate on architectures that aren’t yet scalable.
From what I saw in semiconductors, scaling only happens when manufacturing processes become predictable and cost-effective. Until quantum computing:
✔ Develops a reliable quantum error correction system.
✔ Improves qubit stability and coherence times.
✔ Finds ways to manufacture quantum processors at scale.
It will remain a research tool, not a commercial industry.
Lesson 3: Ecosystem Development Will Be the Biggest Accelerator
Semiconductors didn’t succeed because of hardware alone. The real inflection point came when companies like Intel, AMD, and TSMC built an ecosystem that connected chip designers, software developers, and manufacturers.
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What I Experienced From 1990s Onward
During the 1990s and early 2000s, I saw how the semiconductor industry shifted focus from hardware to software partnerships. The rise of Windows, Linux, and mobile OS platforms meant that semiconductor companies were no longer just making chips—they were powering ecosystems that enabled businesses, developers, and entire industries to grow.
What Quantum Needs to Learn
Right now, quantum computing lacks an ecosystem. Programming a quantum computer requires deep physics knowledge, and businesses struggle to see practical use cases.
To follow the semiconductor playbook, the quantum industry must:
✔ Develop accessible programming environments (like Python did for classical computing).
✔ Partner with enterprise software companies (the way Intel partnered with Microsoft).
✔ Create cloud-based quantum access so businesses can experiment without massive upfront investments.
The quantum companies that focus on building a developer ecosystem will be the ones that dominate.
Lesson 4: Commercial Viability Matters More Than Technical Superiority
The semiconductor industry did not succeed simply because transistors got smaller and faster—it succeeded because companies proved their business value. By the late 1990s and early 2000s, chipmakers had positioned semiconductors as the foundation of personal computing, enterprise IT, and mobile technology.
What I Witnessed in the 2000s and Beyond
By the time smartphones emerged in the late 2000s, semiconductor companies had already built the low-power, high-performance chips that enabled mobile computing. Businesses didn’t need to understand transistor physics—they just needed chips that worked reliably and profitably.
What Quantum Needs to Learn
Quantum computing still lacks a clear commercial path. It needs:
✔ A tangible business case beyond academic experiments.
✔ Hybrid models that integrate classical and quantum computing (just as early microprocessors still relied on mainframes).
✔ Clear ROI for enterprise use cases.
Right now, quantum computing is where semiconductors were in the 1970s—fascinating, but without mass-market applications. Until quantum companies focus on real-world business impact, adoption will remain slow.
Final Thoughts: Quantum’s Future Hinges on Learning From the Past
After three decades in semiconductors, I’ve seen how industries scale, standardize, and commercialize. The playbook for quantum computing already exists—but whether the industry follows it remains to be seen.
✔ Standardization will create confidence in adoption.
✔ Scalability breakthroughs will unlock mass production.
✔ An ecosystem will drive mainstream developer adoption.
✔ Commercial viability will be the real test of success.
If the quantum industry applies these lessons now, we could see quantum computing reach commercial maturity within the decade—just as semiconductors did before it. But if it fails to learn from history, it risks remaining stuck in the lab, rather than changing the world.
The roadmap is clear. The question is: Who will be the Intel of the quantum era?
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About the Author
Shayne De la Force is a globally experienced Chief Marketing Officer and Founding Partner at The CMO Syndicate (www.CMOsyndicate.com), bringing over two decades of operational C-Suite leadership in marketing strategy, advanced manufacturing, and international market expansion. With a career spanning six continents and multiple manufacturing industries, he now drives transformative results for B2B manufacturing clients globally as an independent CMO. Shayne specializes in helping businesses accelerate decision-making and adapt to market challenges with speed and precision. Through The CMO Syndicate, he provides immediate, proven expertise to drive profitable growth, reduce risk, and position organizations for long-term success. Shayne can be contacted at: shayne.delaforce@cmosyndicate.com
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For more on quantum systems, see https://mltblog.com/3DgambA
Quantum Computing Quotes by Top Minds. Collection of quantum computing quotes that will get your mind racing. Imagine a computer that could solve problems in seconds that would take traditional computers years, if not decades to do. That’s the potential of quantum computing. https://www.supplychaintoday.
