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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Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

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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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Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

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Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
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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.

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Xinghui Yin: https://www.linkedin.com/in/xinghui-yin/

Subscribe:
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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.

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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
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.

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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
02:42 - 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

Why are so many companies betting on neutral atoms to build the first useful quantum computers?

In this episode, we speak with Mark Saffman, professor at the University of Wisconsin–Madison and one of the pioneers of neutral atom quantum computing. Over the past two decades, Saffman has helped transform Rydberg atoms from a theoretical idea into one of the leading architectures for scalable, fault-tolerant quantum computing.

We explore the physics of optical tweezers and Rydberg blockade, how neutral atoms perform quantum logic and create entanglement, and why this platform offers unique advantages in connectivity and scalability. Saffman also discusses the engineering challenges of improving gate fidelity, implementing quantum error correction, and scaling from small laboratory experiments to processors containing millions of qubits.

We also discuss the origins of companies like Infleqtion, the rapid growth of the neutral atom ecosystem, and what it will take for quantum computers to solve meaningful scientific and industrial problems.

Whether you're interested in quantum computing, atomic physics, quantum error correction, computer architecture, or the future of information processing, this episode provides a deep technical look at one of the most promising paths toward practical quantum computers.

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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:
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Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro and Reads
02:45 - Neutral Atoms vs Superconductors and Ions
07:30 - Rydberg Atoms
12:49 - Practical Considerations for Rydberg Atoms
19:04 - From Atomic Physics to Quantum Gates
29:49 - Increasing Trap Loading
38:27 - Evolution of Rydberg Gates
45:05 - Limits of Rydberg Fidelity
49:49 - Scaling Neutral Atom Arrays
53:47 - Atomic Species and QEC
1:03:38 - History of Infleqtion
1:10:27 - Mark’s Outlook on the Future
1:15:08 - Caltech and Peter Shor
1:20:00 - Advice for Young Scientists

#quantumphysics #quantumcomputing #physics #computerscience

Why are some of the world's largest technology companies betting on silicon photonics?

In this episode, we speak with John Bowers, professor at UC Santa Barbara and one of the pioneers of silicon photonics, about the technologies that are transforming AI infrastructure and modern data centers. Bowers explains why moving data has become one of the central challenges in computing, how optical communication is overcoming the limitations of traditional electrical interconnects, and why light is increasingly being used to connect processors, servers, and entire data centers.

We explore the origins of silicon photonics, from early optical communications research to the development of integrated photonic devices that can be manufactured using semiconductor processes. Bowers discusses the engineering challenges of combining lasers with silicon, the breakthroughs that enabled heterogeneous integration, and how decades of research helped turn silicon photonics into a commercial technology deployed at global scale.

We examine the growing demands of artificial intelligence, where the movement of information between processors has become just as important as computation itself. Bowers explains why bandwidth, power consumption, and interconnect density are emerging as critical bottlenecks for AI systems, and how optical links are enabling the next generation of large-scale computing architectures.

We also discuss data center networking, optical interconnects, co-packaged optics, heterogeneous integration, semiconductor manufacturing, photonic integrated circuits, telecommunications, AI hardware, and the future of warehouse-scale computing. Throughout the episode, Bowers provides an inside look at how advances in photonics are reshaping the infrastructure that powers modern computing.

Whether you're interested in silicon photonics, optical communications, semiconductor engineering, computer architecture, AI hardware, data center design, networking, integrated photonics, electrical engineering, or the future of computing, this episode provides a deep technical exploration of one of the most important technologies behind the AI revolution.

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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin-168b94130/

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
01:19 - Why Data Centers Need Photonics
05:28 - Bowers's Interest in Physics
10:09 - Lessons From Bell Labs
12:58 - Semiconductor Lasers
18:31 - Teaching Entrepreneurship
23:21 - Heterogeneous Integration
29:40 - Why Silicon Photonics Needed Better Light Sources
32:00 - Heterogeneous Integration vs Direct Growth
44:04 - The Packing Problem in Photonics
47:49 - Narrow Linewidth Lasers
51:31 - Data Centers in Space
59:19 - Lessons from the Telecom Bubble
1:02:17 - Recent Breakthroughs in Photonics
1:04:32 - What is a Frequency Comb?
1:07:07 - Solitons and Microcombs
1:14:48 - Optical Computing and AI
1:19:09 - How Bowers Starts Companies
1:21:56 - Was Bowers Late to Any Trends?
1:22:51 - What would Bowers Build with Unlimited Resources?
1:24:38 - Creating Bell Labs for AI
1:26:35 - Competition, Endurance, and Personality
1:30:41 - The Best Problems for Young Scientists to Tackle
1:37:47 - Advice for Researchers Who Want to Keep Real Depth

#photonics #datacenter #siliconphotonics #computerscience #artificialintelligence

How can a flatworm regenerate a complete head after being cut in half?

In this episode, we speak with Michael Levin, developmental biologist and director of the Allen Discovery Center at Tufts University, about the emerging field of developmental bioelectricity. Levin explains how voltage gradients, ion channels, and gap junctions form a layer of biological control that operates alongside genetics and biochemistry to regulate embryonic development, regeneration, and anatomical patterning.

We explore the experimental foundations of bioelectricity research, including the use of voltage-sensitive dyes, ion channel manipulation, and computational models to read and write electrical information in living tissues. Levin discusses how bioelectric signals help establish left-right asymmetry in embryos, coordinate communication across developing tissues, and encode large-scale anatomical information that individual cells cannot possess on their own.

The conversation examines classic and surprising experiments from the field, including the creation of two-headed planarian worms, the induction of ectopic eyes in frog embryos, and the restoration of normal development after severe genetic and environmental disruptions. Levin explains how bioelectric circuits can act as a control architecture for morphogenesis, allowing tissues to make collective decisions about growth, form, and regeneration.

We also discuss voltage gradients, membrane potentials, gap junction networks, developmental pattern formation, regenerative medicine, collective cellular intelligence, and the relationship between electrophysiology and gene regulation. Throughout the episode, Levin argues that understanding development requires looking beyond genes alone to the dynamic electrical communication networks that coordinate living systems across scales.

Whether you're interested in developmental biology, embryology, regeneration, electrophysiology, bioelectricity, morphogenesis, systems biology, ion channels, pattern formation, or the future of regenerative medicine, this episode provides a deep technical exploration of how electrical signals help shape living organisms.

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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-168b94130/

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
01:40 - Early Interest in Bioelectricity
05:22 - External Electric Stimulation
19:54 - Two-Headed Planarians
31:40 - Designing Bioelectric Experimental Methods
56:37 - Different Model Organisms
1:07:34 - TAME Theory
1:24:16 - Xenobots and Advice for Young Scientists

#planaria #morphology #neuroscience #biology #bioelectricity