15. Directions of TSMC Manufacturing Process for Quantum Processor

If TSMC’s Arizona R&D center pioneers quantum processor manufacturing, it could position itself as a crucial player in the post-classical semiconductor era. However, the transition involves substantial technical and geopolitical challenges. Below is a breakdown of specific quantum technologies that TSMC might focus on:

15.1        Superconducting Qubits Manufacturing

• Why? Superconducting qubits (used by IBM, Google, and Rigetti) are currently one of the most advanced and scalable quantum computing technologies.

• TSMC’s Role: TSMC has experience in advanced semiconductor fabrication at cryogenic temperatures, making it well-positioned to develop high-precision Josephson junctions, superconducting resonators, and cryo-CMOS control circuits.

• Challenges: Extremely low-temperature operation (milliKelvin range) and integration with classical control electronics.

15.2        Silicon Spin Qubits (Quantum Dots & Donor-Based Qubits)

• Why? Silicon-based qubits have longer coherence times and better compatibility with existing semiconductor fabrication techniques. Intel and researchers at UNSW are advancing in this area.

• TSMC’s Role: Its expertise in silicon nanoelectronics could help in fabricating precise quantum dots or donor atom qubits, leveraging CMOS-compatible materials for mass production.

• Challenges: Precise placement of single atoms and scaling up error-corrected quantum processors.

15.3        Photonic Quantum Processors

• Why? Photonic qubits operate at room temperature, reducing the need for extreme cooling, and are promising for quantum networking. Companies like Xanadu and PsiQuantum are pushing this frontier.

• TSMC’s Role: TSMC has experience in silicon photonics, which is crucial for photonic quantum computing, including integrated optical waveguides, modulators, and single-photon sources/detectors.

• Challenges: Lossless photonic components and large-scale entanglement generation.

15.4        Topological Qubits (Majorana-Based Quantum Computing)

• Why? Microsoft is betting on this approach due to its potential for error-resistant quantum computing.

• TSMC’s Role: If breakthroughs occur, TSMC’s expertise in nanofabrication and exotic material deposition (e.g., topological insulators, superconductors) could be leveraged.

• Challenges: Majorana particles remain experimentally elusive, and material engineering is complex.

15.5        Quantum Memory & Hybrid Quantum-Classical Integration

• Why? Even if large-scale quantum computing is distant, hybrid quantum-classical architectures will emerge in high-performance computing (HPC) and AI.

• TSMC’s Role: TSMC could lead in fabricating hybrid chips integrating classical and quantum elements, such as cryogenic memory, quantum interconnects, and quantum-enhanced AI accelerators.

• Challenges: Efficient interfacing between quantum and classical components with minimal noise.

15.6        Strategic Considerations

• Geopolitical Risks: The U.S. government may restrict Taiwan’s access to quantum IP developed in Arizona, impacting TSMC Taiwan’s long-term competitiveness.

• Competition: Established quantum leaders (IBM, Google, Intel, PsiQuantum, etc.) have a head start, and TSMC must decide whether to partner or compete.

• Economic Viability: Quantum computing is still in early stages, and large-scale profitability remains uncertain.

If TSMC successfully transitions into quantum manufacturing, it could sustain its dominance beyond the classical semiconductor era. However, the road ahead is filled with technological and geopolitical uncertainties.

15.7 Detailed Evaluation of the Approach of Silicon Spin Qubit

Is the direction to develop Silicon Spin Qubits (Quantum Dots & Donor-Based Qubits) a correct one for TSMC?  We understand Taiwan government seems to have preference in such a quantum approach (e.g. Taiwan’s national research institute Academia Sinica already developed one qubit based on such technology) since obviously this can take advantage of the CMOS equipment and knowledge that TSMC and her supply chain already have.  Plus, Intel is aiming toward this direction too.  However, like the topological-qubit approach promoted by Microsoft, so far, we don’t see any commercialized quantum computer using this approach out on the market.  Here are detailed evaluations:

The direction of developing Silicon Spin Qubits (Quantum Dots & Donor-Based Qubits) for TSMC aligns well with its existing CMOS expertise and supply chain, making it a logical and potentially cost-effective choice. Given that Academia Sinica has already demonstrated a single qubit based on this technology, and Intel is pursuing a similar approach, this path appears viable. However, key challenges and uncertainties remain:

Advantages of Silicon Spin Qubits for TSMC:

1. CMOS Compatibility – Silicon spin qubits can be manufactured using conventional semiconductor fabrication techniques, leveraging TSMC’s existing infrastructure and expertise.

2. Scalability Potential – In principle, spin qubits can be packed at high density, similar to transistors, enabling better scalability compared to superconducting qubits.

3. Longer Coherence Times – Compared to superconducting qubits, silicon spin qubits generally exhibit longer coherence times, making them promising for fault-tolerant quantum computing.

Challenges & Risks:

1. Difficult Control & Readout – While silicon spin qubits offer good coherence, precise control and error correction remain technically challenging, especially at scale.

2. Slow Development Pace – Compared to superconducting qubits (e.g., IBM and Google’s advances), silicon spin qubits have seen slower experimental progress toward large-scale quantum computing.

3. Uncertain Commercialization Timeline – Unlike superconducting qubits, which are already deployed in cloud-accessible quantum computers, no commercially available system uses silicon spin qubits yet.

Comparison with Microsoft’s Topological Qubits Approach:

• Microsoft’s topological qubit approach (Majorana-based qubits) is theoretically more robust against noise but has not yet demonstrated a working qubit.

• Silicon spin qubits have at least demonstrated single-qubit and two-qubit gates, though full error correction and scalability remain major hurdles.

Final Evaluation:

Silicon Spin Qubits are a promising but long-term bet. TSMC’s investment in this approach could be strategically sound, leveraging Taiwan’s existing semiconductor ecosystem. However, given the slow commercialization of this technology, TSMC should also consider a diversified quantum R&D approach, possibly exploring superconducting qubits or hybrid systems. In the near term, superconducting qubits still dominate the field, and a purely silicon-based quantum roadmap remains risky without significant breakthroughs in control, readout, and scalability.

Detailed Challenges of Silicon Spin Qubits

For silicon spin qubits, let us detail the challenge of (1) Precise placement of single atoms and (2) scaling up error-corrected quantum processors by answering the following 2 questions: For (1), isn't that Rigetti Computing has been using the trap-ion approach successfully which may mean trapping an atom is not that difficult so the the idea can be borrowed by silicon spin qubit approach? For (2), Isn't that Google's Willow chip already used logical qubit to resolve error-correction problem so that the idea can be borrowed by silicon spin approach?

(1) Precise Placement of Single Atoms

The ability to precisely place single atoms (e.g., phosphorus donors or quantum dots) within a silicon lattice is a major challenge for silicon spin qubits because:

• Atomic-Level Precision Required – Silicon spin qubits rely on controlling the interaction between adjacent qubits, which is highly sensitive to atomic-scale variations (sub-nanometer misplacement can significantly alter qubit behavior).

• Dopant-Based vs. Quantum Dot Approach – Donor-based qubits (e.g., phosphorus atoms) require exact placement via ion implantation or scanning probe techniques, which is difficult to achieve uniformly in large-scale production.

• Strain & Defects – Even small defects in the silicon lattice can impact qubit performance, leading to decoherence and unwanted interactions.

Can Trap-Ion Techniques Be Borrowed?

• Ion traps (used by Rigetti, IonQ, etc.) hold ions in a vacuum with electromagnetic fields, allowing precise positioning. However, this works because ions are in a suspended state, free from solid-state defects.

• Silicon spin qubits must be embedded in a solid-state environment, making direct trapping techniques much harder. While some atomic manipulation techniques (e.g., STM-based positioning of atoms) have been demonstrated, they are not yet scalable.

(2) Scaling Up Error-Corrected Quantum Processors

Even if individual silicon spin qubits perform well, scaling up to a large, error-corrected quantum computer introduces significant challenges:

• High-Fidelity Qubit Operations – Google’s Willow chip and its logical qubits use superconducting circuits, which allow rapid, high-fidelity gates. Silicon spin qubits, in contrast, require electron spin resonance (ESR) or magnetic field-based control, which is currently much slower.

• Long-Range Connectivity – Error correction in superconducting qubits (Google’s Sycamore & Willow chips) benefits from flexible circuit-based connectivity, whereas silicon spin qubits rely on nearest-neighbor interactions, making scaling more difficult.

• Fault-Tolerance Thresholds – Google’s logical qubit demonstration showed improved error correction in superconducting circuits, but silicon spin qubits still face hurdles in achieving comparable fidelities and efficient encoding for error correction.

Can Google’s Error-Correction Approach Be Borrowed?

• Yes, in theory, but silicon spin qubits have unique constraints:

  • Superconducting qubits use transmon architectures, where error-correction codes like surface codes are easier to implement due to better connectivity.

  • Silicon spin qubits have lower connectivity and slower gate speeds, making it unclear if Google’s exact approach can be directly applied.

  • Alternative architectures, such as a hybrid system (e.g., using superconducting control interfaces for silicon spin qubits), may be required to make error correction feasible.

Conclusion

Silicon spin qubits have great scalability potential, but precise atom placement and large-scale error correction remain major hurdles. Borrowing ideas from trapped-ion and superconducting qubit systems is possible but challenging due to the fundamental differences in qubit physics and control methods. Hybrid approaches that combine the best aspects of different quantum technologies may be the most viable path forward for TSMC.

15.8 Detailed Hybrid Quantum Approach for TSMC

What are the hybrid approaches that combine the best aspects of different quantum technologies or TSMC? Let us list the possibilities:

Hybrid Approaches for TSMC in Quantum Computing

To leverage TSMC’s existing semiconductor expertise while overcoming the challenges of silicon spin qubits, several hybrid quantum computing approaches could be explored. These approaches integrate the best features of different quantum technologies to improve scalability, control, and error correction.

1. Superconducting-Silicon Hybrid Systems

🔹 Concept: Use superconducting qubits (fast, high-fidelity control) to interface with silicon spin qubits (high scalability, CMOS compatibility).
🔹 Benefits:

• Superconducting qubits can serve as quantum controllers for silicon spin qubits, allowing high-fidelity readout and coupling between distant qubits.

• Silicon spin qubits retain their long coherence times, benefiting from the stability of the silicon environment.

• Compatible with existing superconducting quantum control circuits, such as those used in Google’s Sycamore and IBM’s Eagle processors.
  🔹 Key Challenges:

• Hybridization requires coherent interfacing between the two qubit types, which may introduce loss or decoherence.

🔹 Companies Exploring This:

• QuTech (Delft University) has already demonstrated coupling silicon spin qubits with superconducting microwave resonators.

2. Photonic-Silicon Hybrid Systems

🔹 Concept: Use photonic qubits for long-range quantum communication and error correction, while silicon spin qubits handle computation.
🔹 Benefits:

• Photonic qubits enable fast and lossless quantum communication over long distances, solving the connectivity bottleneck in silicon spin qubits.

• TSMC already has advanced photonic chip fabrication capabilities, making integration feasible.

• Hybrid systems can leverage quantum networking advantages (e.g., optical interconnects between silicon qubit clusters).
  🔹 Key Challenges:

• Efficient spin-photon interfaces need to be developed to link silicon spin qubits with photonic qubits.

🔹 Companies Exploring This:

• PsiQuantum is working on photonic quantum computers that could potentially interface with silicon spin qubits.

3. Topological-Silicon Hybrid Systems

🔹 Concept: Use topological qubits (Majorana fermions) for error correction while utilizing silicon spin qubits for computation.
🔹 Benefits:

• Topological qubits (such as those researched by Microsoft) have intrinsic error protection, reducing the need for large-scale error correction.

• Could allow TSMC to develop smaller, more fault-tolerant quantum processors by combining silicon qubits with topological protection.
  🔹 Key Challenges:

• Topological qubits remain theoretical and have not been fully demonstrated experimentally.

🔹 Companies Exploring This:

• Microsoft is pioneering research into topological qubits, though no commercial implementation exists yet.

4. Trapped-Ion-Silicon Hybrid Systems

🔹 Concept: Use trapped-ion qubits for quantum memory and error correction while relying on silicon spin qubits for computation.
🔹 Benefits:

• Trapped-ion qubits (e.g., from IonQ and Rigetti) are well-developed and highly stable.

• Could serve as error-correcting ancillas for a silicon-based quantum processor.

• Allows for the integration of different qubit modalities (long-lived ion qubits + fast silicon qubits).
  🔹 Key Challenges:

• Hybridization between solid-state (silicon) and atomic-scale (trapped ions) quantum architectures is difficult.

🔹 Companies Exploring This:

• No direct commercial hybrid yet, but research is ongoing in quantum memory integration for hybrid systems.

5. CMOS-Quantum Hybrid Accelerators

🔹 Concept: Instead of building a full-scale quantum computer, integrate quantum accelerators with classical CMOS-based AI processors (e.g., NPU, GPU).
🔹 Benefits:

• TSMC can co-develop AI-optimized quantum chips, enhancing performance for quantum-assisted AI applications.

• No need for full fault tolerance—can use NISQ (Noisy Intermediate-Scale Quantum) processors combined with classical ML models.

• Already aligned with TSMC’s expertise in semiconductor manufacturing for AI hardware.
  🔹 Key Challenges:

• Requires breakthroughs in hybrid quantum-classical algorithms that take advantage of quantum speedup in a practical way.

🔹 Companies Exploring This:

• Google and IBM are developing quantum-AI hybrid models, and TSMC could leverage its AI semiconductor expertise to compete.

Conclusion: What’s Best for TSMC?

Considering TSMC’s strengths in silicon manufacturing, the best hybrid approach could be:
✅ Superconducting-Silicon Hybrid (leveraging CMOS & quantum control)
✅ Photonic-Silicon Hybrid (for long-distance connectivity)
✅ CMOS-Quantum Hybrid Accelerators (AI-quantum integration)

TSMC could combine multiple approaches, first focusing on silicon spin qubits integrated with superconducting controllers, then expanding toward photonic and hybrid AI accelerators as the industry evolves.

15.9 Details about CMOS-Quantum Hybrid Accelerators

15.9.1     CMOS-Quantum Hybrid Accelerators in Short Term

1. Current NISQ (Noisy Intermediate-Scale Quantum) Limitations

   • Present-day quantum processors are still error-prone and limited in scale.

   • Hybrid AI-quantum systems today mostly use quantum computers as co-processors to offload specific tasks like optimization or quantum simulations, rather than replacing classical AI entirely.

   • Companies like Google and IBM are developing quantum-assisted AI models, but these are still in the experimental phase.

2. CMOS is Still Advancing Faster Than Quantum Hardware

   • Traditional AI hardware (e.g., NPUs, GPUs, TPUs) is improving rapidly, making it unclear if quantum processors will offer a clear performance advantage over classical deep learning accelerators.

   • AI hardware follows Moore’s Law, at least for the next decade, meaning classical AI acceleration may continue to outperform near-term quantum AI integrations.

3. Near-Term Applications Are Limited to Specialized Problems

   • Most quantum-AI hybrid models today are focused on optimization, generative models, and physics simulations, rather than broadly replacing neural networks.

   • Classical AI algorithms (e.g., Transformers, Large Language Models) are not naturally suited for quantum speedup, making full quantum AI integration difficult in the near term.

15.9.2     CMOS-Quantum Hybrid Accelerators in Long-Term

Despite these short-term challenges, this hybrid model could still be a long-term strategy if certain breakthroughs occur:

1. Fault-Tolerant Quantum AI

   • If error-corrected quantum computers become feasible, quantum AI accelerators could surpass classical AI in both speed and energy efficiency.

   • Long-term, quantum machine learning (QML) could become a core part of AI acceleration, similar to how GPUs/TPUs revolutionized deep learning.

2. Quantum Advantage for AI Algorithms

   • Research is ongoing to design quantum-native AI architectures that leverage quantum states for exponential speedups.

   • If successful, AI-quantum co-processors could become fundamental for large-scale AI workloads.

3. Hybrid Quantum-Chiplets Integrated into Classical AI Chips

   • Instead of standalone quantum processors, small quantum chiplets could be integrated into AI chips for specialized tasks.

   • This could create heterogeneous computing architectures where classical and quantum circuits co-exist, enabling long-term hybrid AI acceleration.

4. Quantum Photonics Integration for AI

   • Quantum photonics (e.g., light-based AI acceleration) could merge with classical CMOS AI accelerators, improving speed and energy efficiency.

   • TSMC is already experienced in silicon photonics, making this a potential long-term strategy.

Conclusion: An Evolutionary Path

Instead of viewing CMOS-Quantum Hybrid Accelerators as purely short-term, it makes sense to see them as an evolutionary pathway for AI-quantum integration:

• Short-Term (0–10 years) → Quantum accelerators assist classical AI, mostly for niche tasks (optimization, simulations).

• Mid-Term (10–20 years) → Quantum AI becomes more general-purpose, with fault-tolerant quantum co-processors.

• Long-Term (20+ years) → Fully hybrid quantum-AI architectures emerge, with quantum logic deeply embedded in AI chips.

Thus, CMOS-Quantum Hybrid Accelerators could evolve into a long-term model, depending on breakthroughs in error correction, quantum AI algorithms, and photonics integration.