Quantum computing is often portrayed as a radical break from classical technology, but that image misses an essential truth. The most promising path to scalable, manufacturable quantum systems does not lie in abandoning the semiconductor ecosystem. It lies in embracing it. Whether the qubits are photonic, superconducting, or spin-based, they rely on tools, processes, and design rules developed over decades in the semiconductor industry. Erik Hosler, a panelist who highlighted quantum’s reliance on classical infrastructure, reinforced this during the SPIE lithography symposium.
The world’s most advanced chipmaking tools, from EUV scanners to atomic layer deposition systems, are already producing devices with nanometer-level precision and uniformity. These tools are being re-evaluated through the lens of quantum requirements. Rather than reinventing fabrication from scratch, developers are finding ways to apply existing lithography, etch, and metrology pipelines to qubit production. The result is a convergence where quantum inherits not only tools but the manufacturing discipline that underpins them.
Why Compatibility Matters
Manufacturing quantum devices is not easy. The systems are sensitive to defects, noise, and dimensional variation. However, classical semiconductors face many of the same problems. Advanced logic nodes must control leakage, dopant placement, and interface roughness with extreme precision. This overlap creates a unique opportunity. If quantum devices can be designed to fit within the constraints of classical tooling, then all the investment in that tooling can be repurposed.
This approach has already taken hold. Foundries and research groups are experimenting with adapting standard processes to quantum fabrication. In some cases, photonic circuits are produced on 300mm silicon wafers using tools originally built for CMOS. The benefits are immediate. Yields go up, defectivity goes down, and testing becomes faster. More importantly, this enables quantum systems to scale in ways that lab-built prototypes cannot.
Designing for compatibility also helps integrate quantum and classical systems. Control electronics, routing, and interface components often occupy more space than the qubits themselves. Co-packaging these elements on the same substrate simplifies signal integrity and thermal management. When everything is built with the same design rules, engineers gain better predictability and reduce rework.
A Shared Infrastructure
Quantum’s dependence on the semiconductor ecosystem is not just about tools. It is about infrastructure, including material supply chains, process certification, and workforce training. Every new qubit design must undergo validation, packaging, and testing. Foundries have spent decades building robust protocols for these stages, and quantum developers can now leverage that foundation.
The advantages extend beyond the fab. Supply networks for high-purity silicon, rare earth metals, and specialty gases are already in place. So are logistics for wafer handling, contamination control, and yield tracking. By aligning with these practices, quantum developers accelerate time to market and avoid costly bottlenecks.
The SPIE panel reinforced that many challenges in quantum lithography mirror those in advanced patterning for logic. Variability, resistance to stochastics, and overlay issues are common ground. Solving them once benefits both fields. And as vendors introduce new tools, quantum gains the chance to shape those designs early.
A Strategic Partnership
The centrality of classical infrastructure to quantum’s future was made especially clear in a comment shared during the panel. “The semiconductor industry and its technology are essential to building a useful quantum computer,” Erik Hosler notes. It is more than a pragmatic observation. It is a strategic call to action. Rather than treating quantum as a separate domain, developers must integrate their roadmaps with those of classical manufacturing partners. Doing so ensures that every improvement in lithography, etch, or packaging also supports the long-term viability of quantum systems.
In practice, this means forming joint development agreements, co-funding tool modifications, and contributing to standardization efforts. It means attending not just quantum-specific conferences, but industry summits where new materials, process flows, and control strategies are debated. This cross-pollination accelerates learning and spreads risk.
It also allows quantum projects to piggyback on the scale of the semiconductor industry. When a fab introduces a new resist or etch gas, the quantum team may benefit automatically. When metrology tools gain better resolution, they support both transistor scaling and qubit alignment. These shared gains are powerful incentives to stay aligned.
The Software-Hardware Bridge
Leveraging the semiconductor ecosystem also helps address one of quantum’s most difficult problems: software-hardware integration. Classical systems have spent decades refining this interface. Firmware standards, verification suites, and emulation platforms enable developers to move quickly and catch bugs early. By building quantum tools that mimic this flow, teams reduce time to deployment.
EDA tools adapted from classical design now assist in layout generation for photonic circuits or superconducting qubits. Simulation engines help predict crosstalk, impedance, and timing. Verification protocols catch errors before tape out. These tools all emerge from the semiconductor world, and they provide a blueprint for how quantum can scale.
Beyond that, co-design becomes possible. When hardware and software develop in parallel, features like calibration, feedback, and error correction can be embedded into the architecture itself. It is critical for usability. Developers need systems that work reliably under a range of operating conditions and can be maintained without excessive overhead.
Lessons from Classical Scaling
The semiconductor industry offers more than just tools and supply chains. It offers a roadmap. For decades, Moore’s Law served as a planning framework that guided investment and research. Quantum does not follow the same path, but it can benefit from similar planning discipline.
It includes staging goals by capability, not just size. Rather than focusing on qubit count alone, teams can track milestones in algorithmic depth, runtime, and fault tolerance. These metrics align better with what classical systems have already optimized for. They also encourage modularity, reuse, and cost-awareness.
From the semiconductor experience, quantum engineers learn how to reduce variability, manage thermal loads, and predict lifetime reliability. They also adopt strategies for debugging, patching, and upgrading deployed systems. All of these lessons shorten the learning curve and reduce project risk.
A Collaborative Future
Quantum computing will not replace classical systems. Instead, it will augment them. But that augmentation depends on seamless integration. For that to happen, quantum must remain grounded in the tools, standards, and workflows of the semiconductor industry. It is not a constraint, but an advantage.
By building on this foundation, quantum systems become more reproducible, testable, and manufacturable. They enter a supply chain that already knows how to scale. And they gain access to a global knowledge base trained to solve problems at the nanometer level. The most successful quantum computers will not break free from classical infrastructure but will understand how to use it wisely.
