632nm

What does it actually take to build a quantum computer that can scale to millions of qubits?
Learn more about Qolab here: https://qolab.ai/
In this episode, we speak with John Martinis, recipient of the 2025 Nobel Prize in Physics. Martinis is a pioneer in superconducting quantum computing and led the team of engineers at Google Quantum AI during the development of their Sycamore chip, which was the first to demonstrate “Quantum Supremacy,” the outperformance of a quantum computer compared to a classical supercomputer.
John is now the co-founder of Qolab, a company developing new approaches to building scalable superconducting quantum circuits. Martinis discusses why scaling quantum computers is fundamentally an engineering and manufacturing problem, and why the next generation of quantum hardware may require rethinking how the chips themselves are designed and fabricated.
We explore the challenges of building superconducting qubits, from fabrication and packaging to control electronics, wiring, power dissipation, and the subtle imperfections that can determine whether a quantum chip works at all. Martinis explains why adding more qubits is not simply a matter of making existing systems larger. At the scale of hundreds of thousands or millions of qubits, every component has to work together, and improvements in one part of the system can create new problems somewhere else.
Martinis describes the philosophy behind Qolab and its effort to develop a fundamentally different architecture for scalable quantum computing. Rather than simply pushing existing approaches forward, Qolab is trying to remake the individual elements of the system and integrate them in new ways. We discuss wafer-scale fabrication, the challenges of connecting and controlling large numbers of superconducting qubits, and why the manufacturing techniques used to build modern semiconductor chips could be important for the future of quantum computing.
We also discuss the practical engineering lessons Martinis learned while developing superconducting quantum processors, including the difficulty of getting an entire system to work reliably. He recounts the development of the hardware behind Google's early quantum computing efforts, the unexpected failure caused by a circuit board rather than the qubit chip itself, and the many subtle fabrication and engineering issues that can become increasingly important as quantum systems grow larger.
Finally, Martinis explains why he sees quantum computing as a system engineering problem involving dozens of interconnected constraints. From the physics of superconducting qubits to semiconductor fabrication, cryogenic electronics, packaging, and control, building a useful quantum computer requires solving many problems simultaneously. The goal is not simply to build a better qubit, but to develop an architecture that can ultimately support quantum computers at truly large scale.
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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro
01:06 - Secrecy of Fabrication
03:24 - 2 Qubit Gate with Transmons
05:18 - New Knowledge of Superconducting Quantum Computers
08:53 - What are Transmons?
35:56 - Lessons from Failure
38:05 - The Role of Theory in Martinis’ Work
44:51 - Two Level States
48:15 - Engineering Tricks in Superconducting Quantum Computers
53:32 - Metrics for Quantum Success
1:00:11 - Scaling Quantum Computers
1:08:08 - Identifying Sources of Error
1:16:15 - Quantum Supremacy Experiment
1:25:45 - What If Quantum Mechanics Failed?
1:33:10 - Is Quantum Supremacy Holding Up?
1:34:42 - Lift-off Fabrication for Superconducting Quantum Computers
1:41:59 - Quantum Flexibility vs Foundries
1:43:40 - Connecting Distant Qubits
1:47:41 - Codesign for Fault-Tolerance
1:49:16 - Martinis’ Nobel Prize
2:01:55 - Advice for Young Scientists
#quantumcomputing #quantumphysics #superconductor #nobelprize #fabrication

What does a cell actually look like when you can see its molecules in action?
In this episode, we speak with Nobel Prize-winning scientist Eric Betzig, whose pioneering work in super-resolution microscopy transformed our ability to see inside living cells. Betzig recounts his decades-long effort to overcome the diffraction limit of light microscopy, from his early work in near-field microscopy to the development of PALM and his eventual focus on watching biological processes unfold in living cells.
We explore why the familiar picture of the cell in biology textbooks may be fundamentally misleading. Much of cell biology has been built by combining observations from biochemistry, molecular biology, and structural biology to construct models of how molecules interact. But, as Betzig explains, we have historically had very little direct information about the spatial organization and dynamics of these molecules inside a living cell. When he and his colleagues used single-molecule microscopy to watch transcription factors in real time, they found that proteins believed to form stable complexes were instead binding to DNA for only a few seconds, forcing them to reconsider how transcription actually works.
We discuss the diffraction limit, why conventional light microscopes cannot resolve structures at the scale of individual proteins, and how super-resolution microscopy made it possible to study molecular processes with unprecedented spatial and temporal resolution. Betzig also explains why imaging living cells can reveal dynamics that are invisible in fixed samples.
Betzig describes his ambitious Cell Observatory project, which combines automated microscopy, large-scale biological experiments, and artificial intelligence to study the enormous complexity of living cells. Rather than trying to build a “virtual cell” from incomplete measurements, he argues that biology first needs to observe these systems at a much larger scale and turn the resulting data into genuine understanding.
Finally, Betzig reflects on what microscopy has taught him about scientific discovery, why the cell may be the most complex form of matter we know, and why better ways of observing life could fundamentally change our understanding of biology.
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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro
01:30 - The Diffraction Limit
12:30 - Imaging Cells
19:04 - Betzig's Transition from Physics to Biology
32:37 - Getting Fed Up with Science
34:57 - Leaving Science for the Automotive Industry
55:55 - 2008 and the Fall of the Automotive Industry
1:09:50 - Building a Microscope in a Living Room
1:34:01 - Insights from Super-Resolution Microscopy
1:47:53 - AI for Analyzing Petabytes of Data
2:10:35 - Improving Microscopes
2:15:27 - Nuclear Energy and Politics
2:23:18 - The Magic of Bell Labs
2:36:17 - Is SpaceX the New Bell Labs?
#microscopy #cellbiology #superresolution #fluorescence #nobelprize

Why is controlling a quantum hardware becoming one of the biggest challenges in scaling quantum computers?
In this episode, we speak with Yonatan Cohen, co-founder and CTO of Quantum Machines, a company developing advanced control systems for quantum computers. Cohen explains how quantum control sits at the interface between quantum hardware and classical computing, and why this hybrid architecture will become increasingly important as quantum processors scale.
We explore how quantum computers are controlled using precise microwave signals and pulse sequences, the limitations of conventional arbitrary waveform generators, and how Quantum Machines uses FPGA-based pulse processing units to generate waveforms in real time. We also discuss why low-latency classical processing and real-time feedback are essential for calibrating quantum processors, correcting errors, and implementing increasingly complex quantum algorithms. 
Cohen explains what changes when moving from small quantum processors to thousands or millions of qubits, including the challenges of data movement, power consumption, control-channel density, and latency. We also discuss quantum error correction, feed-forward operations, hybrid quantum-classical architectures, and the role of CPUs, GPUs, and FPGAs in stabilizing large-scale quantum systems. 
We also discuss the origins of Quantum Machines, the company's approach to quantum control, and why building scalable quantum computers requires much more than simply increasing the number of qubits.
Whether you're interested in quantum computing, quantum control, quantum error correction, computer architecture, FPGA technology, or the future of fault-tolerant quantum computers, this episode provides a deep technical look at the control infrastructure required to make large-scale quantum computing possible.
Follow us for more technical interviews with the world’s greatest scientists:
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Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro and Reads
02:37 - Quantum Machines and Hybrid Architecture
06:04 - Why Do Classical Computers Need Quantum Processors?
10:02 - State of the Art Controllers
20:02 - Meeting Itamar
24:01 - FPGAs for Quantum
29:29 - Repurposing FPGAs
35:22 - Remote Direct Memory Access (RDMA)
41:29 - What If We Had Perfect Controllers?
43:26 - Adaptive and Embedded Calibrations
47:15 - Picks and Shovels of Quantum Computing
49:14 - Progress in Different Qubits
53:25 - Keeping Up with Quantum News and Research
56:51 - Core Advantages of Quantum Machines
1:00:06 - Managing Larger Teams
1:02:12 - Discovering New Physics with Quantum Machines
1:14:31 - Reinforcement Models in Quantum
1:16:45 - Yonatan’s Intro to Quantum Computing
1:21:24 - Realtime Correction vs Post Processing
1:32:10 - Channel Numbers and Interfering Signals
1:39:38 - Connecting Multiple Modules
1:46:46 - Early Believers in Quantum Machines
1:51:05 - What Would Yonatan Do With Unlimited Resources?
#quantumcomputing #quantumphysics #computerscience #fpga #coding

How did we go from knowing almost nothing about genes to sequencing the entire human genome?
In this episode, we speak with Nobel Prize-winning molecular biologist Walter Gilbert, whose discoveries helped lay the foundation for modern genomics. Gilbert recounts his remarkable journey from theoretical physics into biology, where he helped discover messenger RNA, uncovered the molecular mechanisms of gene regulation, invented one of the first practical methods for sequencing DNA, and later co-founded Biogen, one of the world's first biotechnology companies.
We explore the race to understand how genes work, the search for the elusive lac repressor, how a chance experiment led to the invention of DNA sequencing, and why Gilbert believed decades in advance that sequencing the human genome would transform biology into an information science. He explains the origins of the Human Genome Project, the rise of computational biology, and why today's era of AI-driven genomics was already visible in the earliest DNA sequence databases.
We also discuss the RNA World hypothesis, how life may have begun with self-replicating RNA molecules, the evolution of gene regulation, exon shuffling, the origins of protein domains, recombinant DNA technology, the birth of the biotechnology industry through Biogen, and how scientific revolutions often emerge from unexpected experiments.
Finally, Gilbert reflects on creativity in both science and art, explaining why, after a lifetime of pioneering discoveries, he left the laboratory to pursue digital abstract art.
Whether you're interested in DNA sequencing, the Human Genome Project, molecular biology, biotechnology, computational biology, the origin of life, RNA World, gene regulation, genomics, or the history of modern biology, this episode offers a firsthand account from one of the scientists who helped build the field.
Follow us for more technical interviews with the world’s greatest scientists:
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Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro
02:34 - Jim Watson and Beginning Biology
06:58 - Lac Repressor
15:27 - Picking Good Problems
17:57 - Developing the First Generation of Sequencing
31:09 - The Birth of the Human Genome Project
40:00 - Origins of Life
43:35 - RNA World Hypothesis
54:45 - Experiments vs Theory in Biology
59:07 - Inspiration from Other Discoveries
1:12:08 - Starting Biogen
1:22:24 - Balancing Industry and Academia
1:27:45 - Advice for CEOs
1:32:48 - Perspectives on Art and Science
1:37:49 - Walter’s Journey through Art
1:44:55 - Walter’s Artistic Process and Inspirations
1:50:32 - The Role of Theory in Biology
1:55:51 - Frontiers and Guidance in Science
1:59:37 - Should Everyone Get Sequenced?
#biology #dnasequencing #originsoflife #genetics #humangenomeproject

How do bacteria power one of the most sophisticated molecular machines in nature?
In this episode, we speak with Dr. Michael Manson, one of the pioneers of bacterial motility research, whose nearly 50-year career has helped uncover how the bacterial flagellar motor works. From the first experiments proving that bacterial flagella rotate to the latest breakthroughs in cryo-EM and single-molecule biology, Manson tells the story of how scientists finally solved the mechanism behind a real working biological motor.
We explore how bacteria move through chemotaxis using a biased random walk, why E. coli alternates between running and tumbling, and how individual molecules can control the direction of a spinning flagellum. Manson explains the experiments that showed proton motive force powers the flagellar motor, how the motor’s rotor and stator generate torque, why it can reverse direction almost instantly, and how bacteria adapt to changing environments by dynamically adjusting their molecular machinery.
We also discuss ATP synthase, proton gradients, molecular motors, bacterial genetics, cryo-electron microscopy, ion channels, self-assembling protein complexes, nanomachines, and the history of the discoveries that transformed modern microbiology.
Whether you’re interested in the bacterial flagellar motor, molecular biology, biophysics, microbiology, ATP synthase, chemotaxis, molecular machines, or the fundamental physics of life, this week we go deep into one of biology’s most remarkable inventions.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/
Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/
Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com
Timestamps:
00:00 - Intro and Reads
02:42 - Biased Random Walk
10:27 - Manson's Work with Howard Berg
13:24 - Proton Motive Force and Flagellum
29:07 - Rotors and Stators of Flagella
37:20 - Mot Proteins
57:34 - CheY and Changing Direction
1:11:48 - Biology and Intelligent Design
1:26:52 - Reversing Proton Flow
1:29:59 - Life at Low Reynolds Number
1:39:15 - Mysteries in the 90s and 2000s
1:48:34 - Applications of Understanding the Nanomotor
1:58:33 - Flagellar Motor Crash Course
2:01:46 - Bacterial Learning and Adaptation
2:05:56 - Giving Up on Birds
2:14:09 - Caltech
2:21:38 - Advice for Young Scientists
2:30:02 - Origins of Life
2:31:19 - What's Left for the Flagellar Motor?
PART 2:
2:33:33 - Building the Nanomotor
2:39:29 - Other Types of Flagella
2:47:06 - MotA and MotB
3:07:17 - Reusing Motors Across Biology
3:10:13 - Benefits of Being Small
3:12:34 - CheY and Changing Direction
3:19:39 - How Physics Shapes Evolution