Industry · Early Pilots
Quantum Computing and the Semiconductor Industry
The semiconductor industry has a two-way relationship with quantum computing. Chipmakers could use quantum simulation for materials and process problems, and they are also becoming the manufacturers of quantum hardware itself, from silicon spin qubits to photonic and superconducting chips.
Maturity
Early pilots in simulation; manufacturing of quantum hardware is advancing quickly
Most credible computing use
Simulating materials, defects, and interfaces
Most concrete development
Foundries and chipmakers fabricating qubits, such as silicon spin and photonic qubits
Strongest classical competition
Mature EDA tools, GPU-accelerated lithography, and TCAD simulation
Notable moves
Intel Tunnel Falls, imec and Diraq on 300 mm wafers, IonQ's acquisition of SkyWater
Realistic horizon
Quantum hardware manufacturing now; computing benefits for chip design in the 2030s at the earliest
Overview
Semiconductors are the base of the modern economy, and they are also the most advanced manufacturing technology humans have built. A leading-edge chip involves hundreds of process steps, atomic-scale control of materials, and design problems with billions of components. The industry spends heavily on simulation and optimization, and it is a natural place to ask whether quantum computing could help.
The relationship runs in both directions. In one direction, quantum computers might simulate the materials and defects that limit chip performance, or accelerate parts of design automation. In the other direction, the semiconductor industry is becoming central to quantum computing, because scaling quantum hardware to useful sizes requires fabrication methods, yield control, and supply chains that only the chip industry has. That second direction is where the most concrete developments are happening.
Why this industry is a candidate for quantum computing
At small dimensions, the behavior of transistors, interconnects, and dielectrics depends on quantum effects, and engineers use quantum-mechanical simulation to study defects, interfaces, and new materials. These simulations are costly and use approximations, and for some problems, such as strongly correlated materials and certain interfaces, quantum computers could supply more accurate answers.
Electronic design automation, the software that lays out and verifies chips, relies on large combinatorial optimization and satisfiability problems, which are nominally natural targets for quantum algorithms. However, these tools are highly tuned classical software, and no quantum method has yet matched them. The stronger link is on the manufacturing side: building millions of qubits will need the same tools that built billions of transistors.
Key facts and numbers
| Item | Detail |
|---|---|
| Intel Tunnel Falls | A 12-qubit silicon spin qubit chip released to researchers in 2023 |
| imec and Diraq | Silicon spin qubits on 300 mm wafers with gate fidelities above 99 percent (Nature, 2025) |
| IonQ and SkyWater | IonQ completed its roughly 1.8 billion dollar acquisition of the semiconductor foundry SkyWater Technology in July 2026 |
| EUV lithography | Leading-edge chips are patterned with light of 13.5 nanometer wavelength |
| Quantum Computing Inc. | Operates a thin-film lithium niobate photonic chip foundry in Tempe, Arizona |
| Scale gap | Modern chips hold tens of billions of transistors, while the largest quantum chips have hundreds to a few thousand physical qubits |
Where quantum computing could help
Materials, defects, and process simulation
The problem: New dielectrics, metals, and two-dimensional materials must be understood at the atomic scale. Defects, interface states, and quantum effects in tiny devices determine yield and performance, but accurate simulation is hard.
The quantum approach: Quantum simulation of selected defects and interface models using phase estimation or variational methods, embedded in larger classical simulations.
Where things stand: Demonstrations are small, and classical density functional theory and machine-learned potentials handle most needs. Vendors and chipmakers participate in consortia to define which problems would justify quantum accuracy.
Related algorithms: Quantum Phase Estimation (QPE), Variational Quantum Eigensolver (VQE), Trotter-Suzuki Decomposition
Chip design and verification optimization
The problem: Placing and routing billions of components, verifying logic, and scheduling test patterns are combinatorial problems that existing tools solve heuristically.
The quantum approach: Researchers formulate subproblems as optimization or satisfiability and test them on annealers and QAOA, or consider Grover-style speedups for search.
Where things stand: Results are small and academic. EDA tools are highly optimized and now use machine learning, and there is no evidence of a quantum edge. The classical GPU-accelerated computational lithography tools from companies such as NVIDIA set a high bar for competing approaches.
Related algorithms: Quantum Approximate Optimization Algorithm (QAOA), Grover's Algorithm, Quantum Annealing
Manufacturing quantum processors
The problem: Quantum computers need qubits in large numbers with uniform properties, and the semiconductor industry is the only one with the tools and yield-management experience to produce them.
The quantum approach: Silicon spin qubits are made with CMOS processes, superconducting qubits with thin-film fabrication, and photonic qubits with silicon and lithium niobate photonics. Cryogenic control electronics are built in standard processes.
Where things stand: Intel released a 12-qubit silicon chip in 2023, imec and Diraq showed high-fidelity qubits on 300 mm wafers, and companies are buying or partnering with foundries, including IonQ's acquisition of SkyWater and PsiQuantum's work with GlobalFoundries. Fabrication quality is a main factor in qubit performance.
Related algorithms: Quantum Error-Correcting Codes (Shor Code, Surface Code)
Quantum-safe hardware and supply chain security
The problem: Chips that live in devices for decades, from smart cards to network equipment, must support post-quantum cryptography, and hardware roots of trust need quantum-resistant signatures.
The quantum approach: Accelerators for lattice-based cryptography and hash-based signatures are added to secure elements and processors.
Where things stand: Chipmakers have announced support for NIST's post-quantum algorithms in secure silicon and firmware, and this is a concrete, near-term action for the industry.
Related algorithms: Shor's Algorithm, Quantum Digital Signatures
Who is working on it
- Intel, which built the Tunnel Falls silicon spin qubit chip and cryogenic control chips, and which collaborates with research labs on qubit characterization.
- imec and Diraq, who demonstrated high-fidelity qubits on industrial 300 mm wafers, with Diraq also working with GlobalFoundries.
- IonQ and SkyWater, whose combination gives a quantum company its own U.S. foundry, and Quantum Computing Inc., which operates a photonic chip foundry.
- PsiQuantum and GlobalFoundries, which are manufacturing photonic quantum chips, and IBM, Google, and Rigetti, which operate their own superconducting fabrication lines.
- NVIDIA, whose GPU-based lithography software and quantum-GPU integration efforts connect the two industries.
Realistic timeline
| Horizon | What to expect |
|---|---|
| Now to 2028 | Foundry partnerships and acquisitions continue, silicon and photonic qubit yields improve, and post-quantum support spreads in secure chips. |
| 2028 to 2032 | Quantum chips with thousands of qubits are produced in semiconductor-grade processes. Simulation use for chip materials remains at the pilot stage. |
| 2032 and later | If fault-tolerant machines arrive, quantum simulation might support the design of new materials for devices. EDA advantages remain uncertain. |
How to judge quantum claims in this sector
Statements that quantum computers will redesign chips usually blur the two directions of the relationship. The credible one is that semiconductor manufacturing will make quantum hardware at scale, and that is already happening. The speculative one is that quantum computers will, in turn, accelerate chip design, which has no demonstrated basis and faces strong classical competition.
When assessing a quantum announcement from the chip industry, look at what was actually produced: wafers with measured qubit fidelities and yields are concrete evidence, while roadmaps and partnership announcements are intentions. Metrics such as gate fidelity, uniformity across a wafer, and the cost per qubit tell more than the number of qubits alone.
What stands in the way
- Uniformity and yield: qubits are sensitive to tiny defects, so wafer-level uniformity is a major challenge.
- Wiring and control: connecting millions of qubits to control electronics needs new architectures, such as cryogenic CMOS.
- Competition from classical tools: EDA and TCAD software are strong and improving with machine learning.
- Supply chain and policy: quantum materials, isotopically pure silicon, and cryogenic equipment have limited suppliers and export controls.
What organizations in this sector should do now
- Engage with quantum hardware makers as customers for foundry services, which is the most concrete commercial opening.
- Add post-quantum cryptography support to secure silicon and firmware roadmaps.
- Define materials and defect problems where classical simulation is unreliable, and join consortia that test quantum simulation on them.
- Track fidelity and yield data from fabricated qubits to judge which technologies scale.
Myths versus reality
- Myth: quantum computers will soon design the next processor. Reality: chip design is dominated by tuned classical tools, and a quantum role is unproven.
- Myth: quantum hardware is separate from the chip industry. Reality: scaling depends on semiconductor manufacturing.
- Myth: more qubits means a better chip. Reality: fidelity, uniformity, and connectivity matter as much.
The bottom line
For the semiconductor industry, quantum computing is first a manufacturing opportunity and a security task, and only later a simulation tool. Firms that master qubit fabrication and yield could become suppliers to a future quantum industry, and those that ignore post-quantum support face concrete risk.
Frequently asked questions
Can quantum computers help design chips?
Possibly for materials and defect simulation in the long run. For layout and verification, classical EDA tools are highly optimized and no quantum advantage has been shown.
Are semiconductor companies building quantum hardware?
Yes. Intel built a 12-qubit silicon chip, imec and Diraq produced qubits on 300 mm wafers, and several quantum companies have bought or partnered with foundries.
What is a silicon spin qubit?
A qubit that stores information in the spin of an electron in a silicon structure, made with processes close to those used for transistors.
Why did IonQ buy SkyWater?
To gain a U.S. foundry that can manufacture the chips for its next-generation systems and to supply the semiconductor industry with quantum components.
What should chipmakers do about quantum threats?
Add support for post-quantum cryptography in secure elements and firmware, since devices deployed today may live for decades.
Keep exploring
- Algorithm: Quantum Error-Correcting Codes (Shor Code, Surface Code)
- Algorithm: Quantum Phase Estimation (QPE)
- Algorithm: Quantum Approximate Optimization Algorithm (QAOA)
- Algorithm: Shor's Algorithm
- Hardware: Diraq Silicon Spin-Qubit Devices (imec 300 mm CMOS)
- Hardware: Rigetti Novera QPU
- Hardware: IBM Heron (r1, r2, and r3)
- Company: IonQ
- Company: Quantum Computing Inc. (QCi)
- Company: Rigetti Computing
- Company: IQM
- Research: Independent Verification Attempt of a Recent Quantum Advantage Claim
- Industry: Quantum Computing for Materials Science
- Industry: Quantum Computing, AI, and Data Centers
- Industry: Quantum Computing for Manufacturing
- Industry: Quantum Computing for Cybersecurity
This page is written for educational purposes and summarizes public announcements, published research, and published resource estimates (last reviewed September 30, 2026). Vendor and customer claims have not all been independently verified, timelines are estimates, and nothing here is investment or business advice.