In this episode, Nobel Prize-winning physicist Dr. John Martinis reveals how his breakthrough in superconducting qubits made quantum physics real at macroscopic scale and what it means for the future of technology. The former lead of Google's Quantum AI lab explains why quantum computing is so fragile, why a lot of hype has a low chance to work, and why his fabless company Qolab could be the Nvidia of quantum computing
In this conversation, Dr. Martinis joins Tech Surge to explain the science behind macroscopic
quantum coherence, the engineering challenges of scaling quantum computers, and how hybrid quantum-classical computing will shape the future of technology.
The conversation covers:
✅ How the superconducting qubit breakthrough won the Nobel Prize in Physics
✅ Why Nature wants to destroy quantum coherence and why quantum is fragile
✅ From academic physics to building Google's quantum computer
✅ The engineering challenge of scaling quantum computing beyond the lab
✅ Why a lot of quantum computing hype has a low chance to work
✅ How Qolab's fabless model could scale quantum hardware
Guest Links:
John Martinis: 2025 Nobel Prize laureate in Physics, superconducting-qubit pioneer, former
Google quantum-hardware researcher, and founder and CTO of Qolab.
Nobel Prize profile: https://www.nobelprize.org/prizes/physics/2025/martinis/
Qolab: https://qolab.ai/
Further Reading and Resources
Google Sycamore Quantum Processor - Google’s 2019 experiment used a 53-qubit
superconducting processor to perform a specific random-circuit-sampling task substantially
faster than the then-known classical approach.
Nature research paper:
https://www.nature.com/articles/s41586-019-1666-5
Google Research explanation:
https://research.google/blog/quantum-supremacy-using-a-programmable-superconducting-processor/
Artificial Intelligence and Transformers – The Transformer architecture discussed in the
podcast was introduced in the paper “Attention Is All You Need.”
Original paper:
https://arxiv.org/abs/1706.03762
AlphaFold and Protein Structure Prediction – AlphaFold demonstrated how classical AI can
predict protein structures with high accuracy, illustrating the distinction between present-day AI
and potential future quantum applications.
Nature paper:
https://www.nature.com/articles/s41586-021-03819-2
Google DeepMind – AlphaFold:
https://deepmind.google/science/alphafold/
Quantum Computing Hardware Approaches – The podcast compares superconducting
qubits, semiconductor spin qubits, neutral atoms, trapped ions and photonic systems.
Google Quantum AI:
https://quantumai.google/
Intel Quantum Computing:
https://www.intel.com/content/www/us/en/research/quantum-computing.html
QuEra – Neutral-atom quantum computing:
https://www.quera.com/
Atom Computing:
https://atom-computing.com/
Quantum Manufacturing and Scaling – Qolab is focused on improving the fabrication, wiring and scalability of superconducting quantum processors through industrial partnerships.
Qolab:
https://qolab.ai/
Qolab and Applied Materials collaboration:
https://thequantuminsider.com/2025/03/18/qolab-secures-investment-from-applied-ventures-and-announces-collaboration-to-advance-quantum-computing-manufacturing/
Applied Materials:
https://www.appliedmaterials.com/
Quantum–Optical Networking – The podcast discusses the challenge of converting
microwave signals used by superconducting qubits into optical signals suitable for fiber-optic communication.
Microwave-to-optical conversion research:
https://www.nature.com/articles/s41567-019-0650-1
Chapters:
00:00 – The Quantum Computing Hype: Physics vs Engineering
04:06 – Introducing Nobel Prize Winner John Martinis
12:09 – Schrödinger's Cat Explained
13:12 – Can Quantum Effects Exist at a Macroscopic Scale?
17:45 – The Experiment That Changed Quantum Computing
34:33 – The Biggest Challenge: Scaling Quantum Computers
43:21 – John Martinis on Google's Quantum Supremacy
45:51 – AI vs Quantum Computing
01:01:45 – Can Quantum and Classical Computers Work Together?
01:07:36 – The NVIDIA Model for Quantum Computing
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