632nm
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- 3. Nov. 2026
Über den Podcast
Technical interviews with the greatest scientists in the world.
Folgen 62
Reinforcement Learning and Quantum Decoding | Vlad Sivak Google Quantum AI
How can reinforcement learning help quantum computers learn from their own errors?In this episode, Vlad Sivak, researcher at Google Quantum AI, joins us to discuss how reinforcement learning can improve quantum …
Shownotes
How can reinforcement learning help quantum computers learn from their own errors?
In this episode, Vlad Sivak, researcher at Google Quantum AI, joins us to discuss how reinforcement learning can improve quantum computing, from error correction and decoding to the calibration of quantum hardware. Sivak explains how reinforcement learning allows an AI agent to interact with a quantum system, learn from its errors, and discover how to improve its performance without being explicitly told which actions to take.
We explore how quantum error correction works, why decoding error syndromes is such a challenging problem, and how machine learning can help quantum computers adapt to changing error patterns. Sivak describes his team's work using reinforcement learning to dynamically adjust the weights of a quantum error-correction decoder, allowing it to make better-informed decisions as the properties of the hardware change over time. We also discuss why conventional optimization methods can struggle with quantum systems that are high-dimensional, stochastic, and constantly drifting.
Sivak explains how reinforcement learning can improve quantum hardware calibration by compensating for unknown effects that traditional, model-based approaches may miss. Rather than stopping a quantum computer to run dedicated calibration experiments, the long-term vision is to use the stream of error signals generated during computation to continuously update the system's control parameters. This could eventually allow quantum computers to adapt to hardware drift while continuing to operate.
Beyond quantum computing, we discuss the broader potential of reinforcement learning as a paradigm for autonomous discovery. From AlphaGo's unexpected strategies to recent advances in mathematical reasoning and programming, Sivak explores how systems can learn by experimenting, evaluating outcomes, and discovering solutions for themselves. We examine the challenges of scaling reinforcement learning, the relationship between classical AI and quantum computing, and the possibility of building quantum machines that can learn to maintain and improve their own performance.
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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
00:58 - Protecting Quantum Information
09:28 - Reinforcement Learning for QEC
13:47 - Qubit Calibration
19:10 - Where Do Noise and Error Come From?
21:13 - Scalability of RL Methods
25:28 - Google's AlphaQubit
31:54 - RL vs Human Calibration
33:26 - Settling on RL vs Other ML Approaches
39:57 - Discovery in the Age of AI
44:59 - Collaboration Between Google Deepmind and Quantum AI
46:17 - Combining Quantum and AI
48:35 - Will Human Input Become Obsolete?
55:00 - Controlling Bosonic Codes
1:05:17 - Break Even Point
1:121:51 - Can Syndrome Extraction Be Fault Tolerant?
1:15:41 - Building Gates
1:20:22 - Surface Codes
1:27:38 - Scaling Superconducting Qubits
1:30:22 - How Quantum Decoders Work
1:35:47 - Model Sizes For Larger Quantum Computers
1:37:48 - What Problems Could Be Solved With Unlimited Resources?Where Does Intelligence Live? | Michael Levin on Evolution, AI, and Platonic Space
What is intelligence, and where does it come from?Watch Part 1 here: https://youtu.be/t6EFV2gSSmg?si=VIztbK2CM5Nwj7QAIn this episode, Michael Levin, professor of biology at Tufts University, returns to discuss …
Shownotes
What is intelligence, and where does it come from?
Watch Part 1 here: https://youtu.be/t6EFV2gSSmg?si=VIztbK2CM5Nwj7QA
In this episode, Michael Levin, professor of biology at Tufts University, returns to discuss his research into unconventional intelligence, the origins of cognition, and the surprising ways that biological systems solve problems. Building on his work with xenobots and anthrobots, Levin explores how new forms of intelligence can emerge in engineered biological systems and why understanding these systems may require us to rethink the relationship between evolution, learning, and agency.
We discuss Levin’s concept of "Platonic space": the idea that mathematical truths and other patterns may exist independently of the physical world. Levin proposes that biological organisms, artificial intelligence systems, and engineered machines may act as interfaces through which these patterns can take form. This raises fundamental questions about whether intelligence is something we build, something we discover, or something that becomes accessible when we create the right conditions.
Levin also describes research showing that gene regulatory networks can exhibit learning behaviors, including habituation, sensitization, and Pavlovian conditioning. These findings suggest that learning and problem-solving may occur at levels of biological organization that are often overlooked. We explore how collective systems can acquire capabilities that no individual component possesses, and how this relates to the concept of causal emergence: the idea that a system can have properties and capacities beyond those of its parts.
From xenobots and bioelectricity to hybrid biological systems and AI, we examine how engineered systems can display unexpected behaviors that challenge conventional explanations based on natural selection alone. Levin discusses the possibility of developing tools to detect, communicate with, and harness unconventional forms of intelligence, as well as the potential implications for regenerative medicine, biomedical engineering, and artificial intelligence.
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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:16 - Platonic Space
03:55 - Biology in the Absence of Evolution
21:11 - Extracting Knowledge from Platonic Space
37:04 - Development and Loss Functions
50:09 - Investigating Layers of Control
55:35 - Is Computer Science Misleading Our Understanding of Minds?
58:14 - Bioelectric Patterns and Drug Discovery
1:04:07 - How to Build a Predictive Developmental Model
1:13:20 - Levin’s Top Books, Thoughts on Collective Existence
1:18:00 - Questions that Keep Michael Up at Night
1:22:32 - How Does Levin Interact with AI?#biology #evolution #computerscience #machinelearning #intelligence
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AI, Cancer Vaccines, and the Future of Personalized Medicine | Sid Sijbrandij on GitLab + Biotech
Can AI help design personalized cancer vaccines?Visit Future of Cancer Care Today: https://sijbrandijfoundation.org/fcctIn this episode, we speak with Sid Sijbrandij, founder of GitLab who went "Founder Mode" …
Shownotes
Can AI help design personalized cancer vaccines?
Visit Future of Cancer Care Today: https://sijbrandijfoundation.org/fcct
In this episode, we speak with Sid Sijbrandij, founder of GitLab who went "Founder Mode" against his own cancer. Sid discusses personalized cancer vaccines, tumor sequencing, immunotherapy, and the challenge of making cutting-edge cancer treatments accessible to patients outside traditional clinical trials.
We explore how personalized mRNA cancer vaccines work, from sequencing a tumor and identifying mutations that distinguish cancer cells from healthy cells to selecting tumor-specific antigens that can be encoded into an mRNA vaccine. Sid explains why this approach is fundamentally different from conventional chemotherapy and why engaging the patient's own immune system could give immunotherapies the potential to produce durable or even curative responses.
Sid also describes his own experience combining multiple experimental treatment modalities, including personalized mRNA vaccines, immunotherapies, and radioligand therapy. He explains how radioligands can use molecular binders to selectively deliver radiation to tumor tissue, and how modifying the tumor microenvironment may make cancers more susceptible to subsequent immune therapies.
We also discuss why personalized cancer vaccines remain difficult and expensive to manufacture. In melanoma, recent clinical trials have demonstrated that individualized mRNA vaccines can target dozens of tumor-specific antigens, with surprisingly little overlap between patients. This creates a fundamentally different manufacturing problem from producing a conventional drug at scale: every patient's vaccine may need to be designed and manufactured separately.
Finally, Sid explains his effort to make these technologies more accessible through a network of biotech companies. He describes the economics and logistics of producing personalized cancer vaccines, including his experience reducing the time and cost required to manufacture an individualized vaccine, and why he believes falling sequencing costs, AI-assisted biological analysis, and repeated manufacturing experience could eventually make personalized treatments available to far more patients.
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Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Devan Shah: https://www.linkedin.com/in/shahdevan/
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:10 - mRNA Vaccine Basics
10:20 - Costs of mRNA Vaccine Development
15:59 - Lessons from Building GitLab
19:19 - Standard of Care for Cancer
24:50 - Sid’s Life Story and GitLab
30:49 - Going Founder Mode Against Cancer
42:03 - Selecting Experimental Treatments
50:38 - How Did Sid Know Treatments Were Working?
58:08 - AI for Treatment Assistance
1:02:54 - Making Personalized Treatments Affordable
1:08:17 - Where are the Levers for Improving Drug Development?
1:10:54 - Most Promising Areas of Drug Development
1:17:14 - The Happy Accident of Sid’s Biotech Investments
1:21:07 - Reflecting on Building GitLab
1:22:46 - How Does Sid Start So Many Companies?
1:26:02 - Finding the Right People for Early Companies
1:34:19 - Will the Bahamas be the Next Singapore?
1:38:42 - Advice for Young Scientists#cancer #biology #immunology #cancerresearch #gitlab
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Building Quantum Computers at Semiconductor Scale | John Martinis on Qolab + Superconducting Qubits
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 …
Shownotes
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.comTimestamps:
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
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Diffraction Limit, Microscopy, and Cell Biology | Eric Betzig on Super-Resolution Microscopy
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 …
Shownotes
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.comTimestamps:
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
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The Hybrid Architecture Behind Quantum Computing | Yonatan Cohen Quantum Machines CTO
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 …
Shownotes
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:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
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
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How DNA Sequencing Was Discovered By Accident | Walter Gilbert on Biogen, Industry, and Art
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 …
Shownotes
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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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.comTimestamps:
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
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How Bacteria Evolved Rotary Motors | Michael Manson
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 …
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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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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.comTimestamps:
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 EvolutionKapitel
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The Atomic Physics Behind Neutral Atom Computers | Mark Saffman
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 …
Shownotes
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.
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.comTimestamps:
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
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Silicon Photonics and the Future of AI Scaling | John Bowers
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 …
Shownotes
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.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
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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-168b94130/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
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
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Bioelectricity, Morphogenesis, and Two-Headed Worms | Michael Levin
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 …
Shownotes
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.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
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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-168b94130/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
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
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Quantum Architecture, QAOA, and Cancer Biomarkers | Fred Chong
Are quantum computers changing the way we discover cancer treatments?In this episode, Misha and Yudong spoke with Fred Chong, Seymour Goodman Professor at the University of Chicago, about the future of quantum …
Shownotes
Are quantum computers changing the way we discover cancer treatments?
In this episode, Misha and Yudong spoke with Fred Chong, Seymour Goodman Professor at the University of Chicago, about the future of quantum computer architecture and how quantum algorithms could eventually help solve real-world problems in medicine, optimization, and scientific computing.
Chong explains the transition from the NISQ era toward fault-tolerant quantum computing, why hardware-aware software design remains essential, and how compiler architectures, error correction, and quantum system design all interact across the full computing stack. The conversation explores the challenges of building scalable quantum machines, the tradeoffs between superconducting qubits, trapped ions, and neutral atoms, and why many quantum systems may ultimately function as specialized accelerators alongside classical computers.
We also discuss quantum optimization algorithms like QAOA and how Chong’s group is applying them to cancer biomarker discovery and treatment prediction. By analyzing complex multimodal biological data, including DNA, mRNA, and pathology imaging, these methods aim to uncover patterns that are difficult for conventional machine learning systems to identify without overfitting.
Along the way, Fred shares stories from the early days of supercomputing at Thinking Machines, the origins of his quantum research career, the founding of Super.tech, and his perspective on where quantum computing is genuinely making progress versus where hype still dominates the conversation.
Topics include quantum computing, QAOA, fault-tolerant quantum computing, quantum error correction, quantum compilers, NISQ systems, neutral atoms, superconducting qubits, quantum architecture, cancer biomarkers, biomedical optimization, hybrid quantum-classical systems, and the future of quantum software and hardware co-design.
Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:34 - From Jurassic Park to Quantum Computing
10:13 - Modernizing NISQ Research
13:45 - Designing Around Quantum Hardware
20:30 - Variational Quantum Algorithms
23:07 - Quantum Computers for Cancer Research
30:35 - How Q4Bio Began
37:20 - Will We Need QEC in the Future?
40:25 - What Quantum Computers Can Learn from Classical Architecture
43:08 - Would Fred Return to Classical Computing?
46:11 - Quantum Software and Quantum Compilers
55:19 - Starting Super.tech
1:01:43 - Classical Analogs to Quantum Hardware
1:12:21 - Advice for Young Scientists
1:17:43 - Is AI Impacting Quantum Research?
1:22:38 - Importance of Formal Verification
1:30:40 - QLDPC Codes
1:35:48 - Fred’s Beginnings in Computer Science
1:42:48 - Chicago vs Silicon Valley
1:46:27 - Do We Need More Quantum Software Companies?
1:53:17 - Future of Quantum Computing and Cryptography#quantumcomputing #quantumalgorithms #cancerresearch #computerscience
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How Quantum Sensors Can Measure Single Electrons | Amir Yacoby
How do you measure something as small as a single electron or map quantum behavior at the nanoscale?In this episode, Misha spoke with Amir Yacoby, professor at Harvard University, about the cutting edge of …
Shownotes
How do you measure something as small as a single electron or map quantum behavior at the nanoscale?
In this episode, Misha spoke with Amir Yacoby, professor at Harvard University, about the cutting edge of quantum sensing and the experimental tools redefining how we probe the quantum world.
Yacoby explains how physicists build ultra-sensitive detectors, from single-electron transistors to quantum dots and NV centers in diamond, that can measure charge, spin, and magnetic fields with extraordinary precision. These tools make it possible to study both strongly correlated systems, like those exhibiting the fractional quantum Hall effect, and isolated quantum systems used as qubits.
We explore how accidental discoveries in the lab can evolve into entirely new sensing techniques, including momentum-resolved tunneling and nanoscale imaging methods. The conversation also highlights how quantum sensors are enabling researchers to bridge two regimes: complex many-body systems and controllable quantum devices, opening the door to new insights in topological physics and quantum information processing.
Whether you're interested in quantum measurement, nanoscale imaging, or the future of quantum technologies, this episode offers a detailed look at how new instruments are driving discovery at the frontiers of physics.
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Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
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Xinghui Yin: https://x.com/XinghuiYin
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
01:23 - The Process of Creating Quantum Tools
11:28 - Graduate School at Weizmann
14:51 - From Aerospace to Condensed Matter
26:53 - Starting at Harvard
39:44 - Working at Bell Labs
47:42 - Diamond NV Centers
1:00:52 - Spin Waves
1:16:10 - SQUIDs
1:29:57 - State of the Art Sensors
1:33:08 - Motivations for Building Better Sensors
1:36:52 - Fabrication Challenges
1:40:14 - New Sensors
1:45:49 - Majoranas
1:53:25 - Finding New Applications for Sensors
1:57:16 - The Use of AI in Physics
1:58:55 - Advice for Young ScientistsKapitel
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The Physics of Un-Hackable Face Recognition | Rob Devlin on Metalenz
How do you turn a flat piece of nanostructured material into a secure biometric sensor?In this episode, we speak with Rob Devlin, co-founder and CEO of Metalenz, about how metasurfaces are transforming optics …
Shownotes
How do you turn a flat piece of nanostructured material into a secure biometric sensor?
In this episode, we speak with Rob Devlin, co-founder and CEO of Metalenz, about how metasurfaces are transforming optics and enabling a new generation of biosecure sensing. Devlin explains how engineers can control light at the subwavelength scale to replace bulky lens stacks with a single flat surface, and why the real breakthrough isn’t just miniaturization, but the ability to mass-produce optics in semiconductor fabs.
We explore how Metalenz scaled metasurfaces from academic prototypes into millions of devices, and what it takes to design optics for manufacturing. Devlin breaks down the transition from building one perfect device in a cleanroom to producing millions that all meet tight specifications.
The conversation focuses on polarization imaging as a new information channel in consumer devices. Unlike traditional cameras that capture only intensity and color, polarization reveals material properties. This enables a new approach to facial recognition that is both more secure and more compact than existing systems.
Rob also shares the story behind Metalenz, from its origins in a Harvard lab to partnerships with major semiconductor manufacturers, and how the company navigated the challenges of finding product-market fit, scaling fabrication, and building a new sensing stack from scratch.
Whether you’re interested in optics, nanofabrication, consumer electronics, or the future of biometric security, this episode explores how controlling light at the nanoscale is opening entirely new possibilities for sensing and identity verification.
Follow us for more technical interviews with the world’s greatest scientists:
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
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Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:22 - Making Metalenses Mass-Producible
10:58 - Metasurfaces for Polarimetry
17:10 - Face ID Security and Pitfalls
24:47 - Polar ID Principles
29:02 - Polar ID Demo
39:58 - Meeting Federico Capasso
50:43 - Developing Metasurface Fabrication Techniques
55:58 - Founding Metalenz
1:11:44 - Future of Metalenz and Metasurfaces#photonics #faceid #biometrics #metasurface #biosecurity #optics
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The Real Economics of Data Centers in Space | Starcloud CEO Philip Johnston
Are data centers in space physically possible, or just another overhyped idea?In this episode, we speak with Philip Johnston, CEO of Starcloud, about the technical and economic case for putting AI …
Shownotes
Are data centers in space physically possible, or just another overhyped idea?
In this episode, we speak with Philip Johnston, CEO of Starcloud, about the technical and economic case for putting AI infrastructure in orbit. The idea has gone viral in recent months, drawing strong criticism from science communicators like Scott Manley, Kyle Hill, and Hank Green, but rarely with detailed engagement on the underlying assumptions.
We examine whether space-based data centers can compete with terrestrial infrastructure, and what constraints actually matter: energy generation, cooling, launch costs, and manufacturing at scale. Johnston walks through the core economic model behind Starcloud, including assumptions about SpaceX’s Starship, the cost of solar power in orbit, and why removing terrestrial constraints like land use, permitting, and energy storage could fundamentally change how compute is deployed.
We discuss the physics of radiative cooling in space, the challenges of operating GPUs in a radiation environment, and how orbital systems compare to Earth-based data centers in terms of efficiency and cost structure. The conversation also explores broader questions around AI’s growing energy demands, the limits of terrestrial infrastructure, and whether shifting compute off-world is a niche solution or a long-term inevitability.
Whether you’re interested in space technology, AI infrastructure, energy systems, or the economics of large-scale computing, this episode offers a detailed look at one of the most debated ideas in modern engineering, and a rare opportunity to hear its strongest arguments laid out in full.
Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
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Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:12 - What is Starcloud?
02:44 - Why do data centers need to go to space?
06:15 - Can’t we just build more solar panels on earth?
11:10 - Economic analysis of Starcloud
19:56 - How does Starcloud’s cooling work?
28:26 - Training an LLM in space
32:07 - Addressing critics on space Twitter
34:23 - Is Starcloud overfunded?
35:59 - Will demand for data centers keep going up?
38:11 - GPU lifespan and disposal in space
39:47 - Bus structures
41:43 - Starcloud’s origin and founders
49:29 - Fundraising, Competition, and Meeting Expectations
53:29 - Satellite size and collisions
56:29 - Manufacturing Bottlenecks
1:00:20 - Starcloud 1 tests
1:01:57 - Acceleration after YC
1:03:43 - Testing on Earth
1:05:06 - Motivations for Starcloud
1:06:45 - Data centers on the Moon
1:08:12 - Interacting with AI companies
1:08:18 - What’s next for Starcloud?
1:14:01 - Other uses for Starcloud satellites
1:17:56 - Lunar hotels and space elevators
1:24:28 - Complementary business ideas to Starcloud
1:29:51 - Philip’s competitive twin
1:32:18 - Philip and Mike’s thoughts on YC
1:34:45 - Advice for young entrepreneurs#datacenter #aidatacenter #starlink #spacex #falcon9 #starcloud
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How To Make Quantum Algorithms Cheaper | Craig Gidney on Magic-State Factories, Resource Estimates
How do you actually make quantum algorithms work on real hardware?Build your own quantum circuits in Crumble: https://algassert.com/crumbleIn this episode, we speak with Craig Gidney of Google Quantum AI, whose …
Shownotes
How do you actually make quantum algorithms work on real hardware?
Build your own quantum circuits in Crumble: https://algassert.com/crumble
In this episode, we speak with Craig Gidney of Google Quantum AI, whose work focuses on the practical realities of building fault-tolerant quantum computers. Gidney explains how seemingly small implementation choices, like how you perform arithmetic, can dominate the cost of entire quantum algorithms.
We explore why factoring small numbers like 15 in Shor's algorithm can be misleadingly easy, and why scaling to larger numbers requires dramatically more resources due to operations like modular multiplication. He breaks down how quantum circuits are often dominated by classical reversible logic, and why optimizing these routines is critical for making quantum computing viable.
The conversation covers quantum error correction, including why T gates are especially expensive, how magic state factories works, and how different hardware architectures change what “cost” even means. Gidney also explains how resource estimates for breaking cryptography have dropped by orders of magnitude and what drove those improvements.
We also dive into the tools he built, including Stim, Quirk, and Crumble, which help researchers simulate noise, visualize circuits, and track how errors propagate through complex systems. Gidney shares his unconventional path into the field, the role of intuition and tooling in discovery, and how software engineering shapes modern quantum research.
Whether you’re interested in quantum computing, error correction, cryptography, or the engineering challenges behind scalable quantum systems, this episode offers a clear and grounded look at what it really takes to turn quantum algorithms into reality.
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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:22 - Shor’s Algorithm
04:02 - Why are Arithmetic Operations Important?
08:35 - Why are T-Gates Important for QEC?
13:47 - Motivations for Creating Crumble and STIM
18:40 - Can AI Code Quantum Simulators?
22:32 - Journey into Learning Quantum
26:50 - How to Enter the Field of Quantum Computing
31:16 - From Starcraft to Software Engineering
36:05 - Crumble Demo
53:18 - Quirk Demo
1:00:48 - Estimating Resources for Quantum Computation
1:08:58 - Optimizing Measurements for Computation
1:16:40 - How Many Qubits Do We Actually Need?
1:30:49 - Other Research Areas for Improving Fault Tolerance
1:41:23 - Elliptic Curve Discrete Logarithm Problem
1:46:55 - New Tools for Quantum Computing
1:50:23 - What Would Craig Do with Unlimited Funding?
1:52:28 - How Learning Has Changed for Craig with Experience
1:57:31 - Riding the Wave of Innovation vs Sticking to One Idea
1:59:53 - Advice for Young Scientists#quantumcomputing #quantumphysics #computerscience #googleai #googlequantum
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How Neurons Translate Electricity into Chemistry | Tom Südhof
How do neurons convert electrical signals into chemical messages in under a millisecond?In this episode, we speak with Thomas Südhof, Stanford neuroscientist and Nobel laureate whose discoveries revealed the …
Shownotes
How do neurons convert electrical signals into chemical messages in under a millisecond?
In this episode, we speak with Thomas Südhof, Stanford neuroscientist and Nobel laureate whose discoveries revealed the molecular machinery that allows neurons to communicate at synapses. Südhof explains how an electrical impulse traveling down a neuron triggers the rapid release of neurotransmitters, transforming an electrical signal into a chemical one that can be received by the next cell.
We explore the remarkable precision of synaptic transmission, including how calcium ions trigger vesicle fusion, how specialized proteins organize the release machinery, and why this entire process unfolds on the timescale of a single millisecond. Südhof walks us through the molecular components that make this possible, including the proteins that dock neurotransmitter-filled vesicles and control their release.
The conversation also examines how these discoveries reshaped modern neuroscience by revealing the fundamental mechanisms underlying neuronal communication. Südhof discusses how synapses operate as highly specialized molecular machines and how disruptions in synaptic signaling are linked to neurological and psychiatric disorders.
Whether you’re interested in neuroscience, synapses, brain signaling, neurotransmitters, or the molecular basis of thought, this episode offers a clear explanation of how neurons translate electricity into chemistry, and how this microscopic process makes brain communication possible
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Mikhail Shalaginov: https://x.com/MYShalaginov
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Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 - Intro
01:23 - What is a Synapse?
07:01 - History of Synapse Discovery
12:54 - How Electron Microscopy Helped Neuroscience
15:11 - Early Electrophysiological Experiments
18:31 - Why are Neurotransmitters Needed At All?
21:25 - Electrical Connections Between Cells
22:48 - How Signal Diversity is Created in Synapses
29:04 - Why are Synapses Chemical?
31:06 - How Tom Began his Neuroscience Career
39:32 - Emerging Tools that Allowed for Researching Synapses
44:16 - Discerning Protein Function
49:36 - Discovering Mechanism through Data
52:15 - Isolating Membrane Proteins
55:09 - Voltage Gates
57:50 - How Synapses Change Over Time
1:02:14 - How are Synapses Formed?
1:10:22 - The Need for New Tools
1:11:53 - Implications for Drug Discovery
1:17:07 - Exploring the Mouse Hippocampus
1:22:35 - Tom’s Work on LDL Receptors
1:26:33 - Understanding Molecular Logic#neuroscience #neuroplasticity #nobelprize #hubermanlab #neurobiology
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How Engineers Solve “Impossible” Problems | Dan Gelbart
How do engineers solve problems that seem to violate the laws of physics?In this episode, we speak with Dan Gelbart, a prolific inventor and precision engineer, about what it really means to work at the limits …
Shownotes
How do engineers solve problems that seem to violate the laws of physics?
In this episode, we speak with Dan Gelbart, a prolific inventor and precision engineer, about what it really means to work at the limits of physical law. From lasers and optical systems to ultra-precision manufacturing and semiconductor tools, Gelbart has spent decades designing systems where nanometers, noise, and nonlinearities matter, and where small misunderstandings of physics can block real progress.
We discuss the story of the first working laser, built by Theodore Maiman, and why it succeeded only after questioning widely accepted assumptions. Gelbart explains how many “impossible” engineering problems aren’t forbidden by physics at all: they’re constrained by measurement errors, incomplete models, or failure to explore edge cases like pulsed operation, material effects, and boundary conditions.
We explore precision metrology, high-resolution imaging for satellite systems, the culture of engineering education, and the difference between a true physical limit and a design constraint. Gelbart reflects on why mastering fundamentals, mechanics, optics, electromagnetism, matters more than chasing trends, and how breakthroughs often come from carefully re-examining what others assume cannot be done.
Whether you’re interested in physics, engineering, semiconductor manufacturing, lasers, or the philosophy of technological innovation, this conversation offers a rigorous look at how engineers operate at the edge of what nature allows, and sometimes push beyond what others think is possible.
Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 - Intro
01:35 - The World’s First Laser
07:53 - Solving Impossible Problems
23:37 - Underestimated Problems
39:36 - Dan’s Backstory
43:33 - How to Teach Yourself Anything
47:03 - Shortcomings of Modern Education
53:19 - Developing the Optical Tape Recorder
1:01:39 - Machine Obsolescence
1:08:04 - Why are Scientists Often Bad Businessmen?
1:15:17 - Developing Medical Devices
1:24:52 - Untapped Potential of Materials Science
1:30:47 - Accidental Inventions
1:35:37 - Surviving Bureaucracy
1:42:27 - Humanoid Robots
1:44:11 - Managing an Engineering Team
1:50:06 - Developing the First Good Mobile Data Terminal
1:54:15 - Building an Environment for Solving Problems
2:02:18 - Why Aren’t We Inventing New Things?#machining #cnc #precisionengineering #metrology #machineshop
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How Visual Experience Rewires the Brain | Mark Bear on Neuroplasticity
How does experience rewire the brain—and why is vision the ideal system for understanding neuroplasticity?In this episode, we speak with Mark Bear, MIT neuroscientist and a pioneer in the study of …
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How does experience rewire the brain—and why is vision the ideal system for understanding neuroplasticity?
In this episode, we speak with Mark Bear, MIT neuroscientist and a pioneer in the study of experience-dependent plasticity. Bear explains how the visual cortex became a model system for uncovering the synaptic mechanisms that allow the brain to change, adapt, and learn, especially during early development.
We explore how visual experience shapes neural circuits, why the brain undergoes critical periods of heightened plasticity, and what classic experiments in visual deprivation revealed about how connections are strengthened or lost. Bear walks us through the discovery of binocular vision in the cortex, the role of inhibition in closing critical periods, and how these ideas reshaped our understanding of learning and memory.
The conversation also covers modern views of cortical plasticity, including perceptual learning, visual recognition memory, and how the brain distinguishes familiar from novel stimuli. Bear discusses how insights from vision extend to broader questions about brain development, neurological disorders such as amblyopia, and whether adult plasticity can be reopened.
Whether you’re interested in neuroscience, brain development, neuroplasticity, learning and memory, or the biology of vision, this episode offers a clear and authoritative look at how experience shapes the brain at the level of neural circuits and synapses.
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://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
00:54 - Neuroplasticity in the Visual Cortex
05:45 - Critical Periods for Neuroplasticity
16:50 - Brain Development in Blind People
19:25 - Hallucinations and Sensory Deprivation
25:36 - How Mark’s Vision Disorder Led Him to a Career in Neuroscience
31:35 - Intro to the Visual System
35:52 - Visual System Processing
40:52 - Pop Science Neuroplasticity
45:00 - Memory Enhancing Pharmaceuticals
50:18 - Other Ways of Modifying the Visual Cortex
1:14:50 - Declarative vs Procedural Memory
1:22:36 - Neural Networks and Memory Degradation
1:25:16 - Neuron Transplants and Neurogenesis
1:28:58 - Brain-Machine Interfaces
1:33:46 - Most Prominent Issues in the Field
1:40:47 - Fragile X Syndrome
1:51:10 - Advice for Young Scientists#neuroplasticity #neuroscience #hubermanlab #braindevelopment #brainplasticity
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Snell's Law, Metasurfaces, and Metalenses | Federico Capasso
How can flat surfaces shape light as powerfully as bulky lenses?In this episode, we speak with Federico Capasso, Harvard physicist and pioneer of metasurfaces, metalenses, and nanophotonics. Capasso traces the …
Shownotes
How can flat surfaces shape light as powerfully as bulky lenses?
In this episode, we speak with Federico Capasso, Harvard physicist and pioneer of metasurfaces, metalenses, and nanophotonics. Capasso traces the path from his work at Bell Labs on quantum cascade lasers to the invention of metasurface optics, showing how a practical challenge—collimating light without traditional lenses—sparked a new way to control light.
We explore the physics behind metasurfaces and generalized Snell’s law, explaining how subwavelength structures enable precise control of wavefronts, phase, and polarization beyond what conventional diffractive optics or Fresnel lenses allow. Capasso clarifies common misconceptions, contrasts metasurfaces with diffraction gratings and phased arrays, and emphasizes the importance of physical intuition and simplicity.
The conversation covers metalenses, polarization optics, holography, and how these ideas moved from theory to large-scale manufacturing in semiconductor foundries, ultimately appearing in consumer devices like smartphones. Capasso also reflects on commercialization, the legacy of Bell Labs, and the blurred boundary between basic science and real-world technology.
Whether you’re interested in metasurfaces, metalenses, nanophotonics, optics, or the process behind breakthrough discoveries, this episode offers a clear and insightful look at how modern optical physics becomes transformative technology.
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://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 - Intro
01:53 - Transition from Bell Labs to Harvard
09:45 - Generalized Snell's Law
21:25 - Facing the Diffractive Optics Community
31:07 - Benefits of Well-Rounded Education
45:16 - Metalenses
55:55 - Can AI do Physics?
1:07:39 - Industry vs Academia
1:11:44 - Nanophotonics
1:14:44 - What Allowed for the First Metalenses?
1:17:38 - 632nm and Other Lasers
1:20:47 - Quantum applications of Metalenses
1:30:14 - Quantum Entanglement Redefines Spacetime
1:43:22 - Stokes Parameters
1:48:28 - Limits of Metasurface Pixel Size
1:55:20 - Advice for Young Scientists
2:01:45 - Critique of the H Index#metasurface #metalenses #quantumphysics #materialscience #optics #photonics
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Graphene, Nanotubes, and Quantum Hall Physics | Philip Kim
How do electrons behave when they’re confined to a single layer, and why do entirely new laws of physics emerge when dimensions shrink?Papers discussed in this episode:Experimental observation of the quantum …
Shownotes
How do electrons behave when they’re confined to a single layer, and why do entirely new laws of physics emerge when dimensions shrink?
Papers discussed in this episode:
Experimental observation of the quantum Hall effect and Berry's phase in graphene: https://www.nature.com/articles/nature04235
Tunable Fractional Quantum Hall Phases in Bilayer Graphene: https://arxiv.org/abs/1403.2112
Room-Temperature Quantum Hall Effect in Graphene: https://arxiv.org/abs/cond-mat/0702408In this episode, we speak with Philip Kim, Harvard physicist and a leading experimentalist in low-dimensional quantum materials. Kim traces the experimental path from high-temperature superconductors and charge-density waves to carbon nanotubes and the earliest graphene devices, revealing how advances in nanofabrication and quantum transport opened the door to modern 2D materials physics.
We dive deep into the Hall effect and quantum Hall effect, from their 19th-century origins to the discovery of quantized and fractional conductance, and explain why these effects were found experimentally before they were fully understood theoretically. Kim shares behind-the-scenes stories of early graphene experiments, mechanical exfoliation, Shubnikov–de Haas oscillations, and what it was like to be scooped by the work that launched graphene into the spotlight.
Along the way, we explore how disorder, dimensionality, and magnetic fields shape electronic behavior; why carbon nanotubes paved the way for graphene; and how many of the most important discoveries in condensed matter physics arise from intuition, timing, and new experimental tools.
Whether you’re interested in graphene, quantum transport, the quantum Hall effect, nanofabrication, superconductors, or the real stories behind breakthrough discoveries, this conversation offers a rare, technically rich look at how modern quantum materials research actually unfolds.
Follow us for more technical interviews with the world’s greatest scientists:
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Mikhail Shalaginov: https://x.com/MYShalaginov
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Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 - Intro
01:15 - How Philip Began Studying Graphene
20:06 - Old Methods of Creating Graphene
32:33 - Hall Effect and Quantum Hall Effect
48:29 - Philip's Work at Columbia
52:33 - Philip's First Experiments with Graphene
1:06:43 - Did Philip Get Scooped from a Discovery?
1:09:40 - The Power of Scotch Tape
1:24:57 - High Temperature Quantum Hall Effect
1:30:18 - Fractional Quantum Hall Effect
1:41:17 - Collaboration with Particle Physicists
1:54:13 - Single Layer Graphene
1:59:44 - Next Gen Electronics with 2D Materials
2:03:23 - Graphene Twisting
2:14:48 - Superconductivity in Other Materials
2:20:06 - Anyons
2:30:00 - Fault-Tolerant Quantum Computing
2:36:05 - Can AI and Big Data Help Physicists?
2:40:47 - What Would Philip Do with Unlimited Resources?
2:43:44 - Optimizing the Education System#graphene #quantumphysics #materialscience #halleffect #electromagnetism
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Quantum Matter, Super-conductors, and Black Holes | Subir Sachdev on the SYK Model
What makes high-temperature superconductors and “strange metals” some of the most perplexing systems in modern physics?In this episode, we speak with Dr. Subir Sachdev: Harvard physicist and one of the leading …
Shownotes
What makes high-temperature superconductors and “strange metals” some of the most perplexing systems in modern physics?
In this episode, we speak with Dr. Subir Sachdev: Harvard physicist and one of the leading architects of today’s understanding of quantum matter. Sachdev explains why strange metals refuse to behave like ordinary conductors, how quantum entanglement reshapes the landscape of many-body physics, and why the quest to understand cuprate superconductors continues to push both theory and experiment to their limits.
We explore the physics of the cuprate phase diagram, the collapse of quasiparticles, and the role of quantum criticality in creating universal, linear-in-temperature behavior. Sachdev walks us through the origins of the SYK model, its surprising connections to black-hole thermodynamics and holography, and how new lattice-based models may finally bridge the gap between solvable theory and real materials.
Whether you’re curious about superconductivity, quantum criticality, black-hole analogies, emergent gauge fields, or the deep physics behind strongly correlated electrons, this conversation offers a rare, accessible look at how frontier theoretical work is redefining our picture of quantum matter—from the lab bench to the edge of spacetime.
Follow us for more technical interviews with the world’s greatest scientists:
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Substack: https://632nmpodcast.substack.com/Follow our hosts!
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Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
01:22 - Subir’s Path to Condensed Matter Physics
06:24 - Challenges in Discovering Cuprates
09:53 - History of Superconductivity
20:07 - Subir's PhD work
27:09 - Development of the SYK model
41:09 - Strange Metals
56:43 - Derivation of SYK Model
1:03:53 - Signatures of Strange Metals
1:09:58 - How Quantum Mechanics Affects Black Holes
1:17:10 - What Brought Subir to Black Holes?
1:19:43 - Black Hole Connections to SYK
1:29:28 - ADS CFT Correspondence
1:37:04 - Can Quantum Computers Help Advance the SYK Model?
1:40:17 - Is AI Useful for Theoretical Physics?
1:46:40 - How does Quantum Criticality Play into Superconductivity?
1:49:11 - Derivation Quantum Criticality
1:52:49 - What is Holography?
1:55:07 - Holography
2:00:19 - Green’s Function
2:08:46 - Green’s equation slides
2:13:23 - Yukawa Model vs SYK
2:17:30 - Can AI Brute Force Physics Discoveries?
2:23:51 - What Would Subir Do With Unlimited Funding?
2:36:33 - Dissecting the Hype of Superconductivity
2:31:15 - Raising the Next Generation of Great Physicists#theoreticalphysics #quantummaterials #astrophysics #superconductivity #superconductor #blackhole #quantumphysics #quantummechanics
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How to Build Fault-Tolerant Quantum Computers | Austin Fowler on Surface Codes + TQEC
Would we get a quantum computer sooner if everything was open source?In this episode, we speak with Austin Fowler, one of the architects of quantum error correction and a pioneer of the surface code used in …
Shownotes
Would we get a quantum computer sooner if everything was open source?
In this episode, we speak with Austin Fowler, one of the architects of quantum error correction and a pioneer of the surface code used in today’s leading quantum computers. Fowler helped lay the groundwork for scalable, fault-tolerant computation at Google Quantum AI, before leaving to advocate for a more open and collaborative model of research.
He explains why building a useful quantum computer will require millions of reliable qubits, why no known algorithm yet clearly outperforms classical computation, and why the field’s current competitive funding model may be slowing progress instead of accelerating it. From the engineering challenges of superconducting qubits to the economics of global research, Fowler offers a candid, inside look at the state of quantum technology.
We explore the history and promise of quantum error correction, the software bottlenecks that still stand in the way, and how an open-source, international approach — modeled on CERN or the International Space Station — could transform the field. Along the way, Fowler reflects on his time at Google, the importance of collaboration, and what it will really take to make quantum computing practical.
Whether you’re interested in quantum hardware, physics, computer science, or research policy, this conversation reveals the technical, ethical, and economic realities behind one of today’s most ambitious scientific pursuits.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
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Michael Dubrovsky: https://x.com/MikeDubrovsky
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Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 - Intro
01:40 - Austin’s Longevity in Quantum
02:31 - What’s the Goal of Quantum Computing?
05:01 - Creating Fault-Tolerant Qubits
06:55 - Advantages of 2D Surface Code
08:47 - Austin’s Journey into Quantum
16:32 - Working at Google
20:14 - Alternatives to Surface Codes
22:18 - Should Quantum Computing Be Open Source?
25:20 - Quantum Computing is Eating Itself
30:52 - Open Source as a Mission
35:46 - Advice for People Getting into TQEC
39:03 - Bit Flips vs Phase Flips
45:43 - History of Surface Codes
49:05 - From Surface Code to Fault Tolerance
57:19 - What Software do Quantum Computers Need?
1:00:17 - Quantum vs Classical Error Correction
1:05:57 - Manufacturing Superconducting Qubits
1:12:02 - Noise Models in Software
1:21:21 - How do NISQ Experiments help us Build Better Computers?
1:24:01 - State of the Art Topological QEC
1:31:38 - How did the TQEC Community Begin?
1:34:46 - Future of TQEC
1:36:03 - Quantum AI
1:37:58 - Advice for Young Scientists
1:41:35 - Underrated Quantum Research
1:47:21 - What are the Most Important Upcoming Developments?Kapitel
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Why Syncing Atomic Clocks is Virtually Impossible | Judah Levine on UTC
Why is syncing atomic clocks still one of the hardest problems in physics and engineering?In this episode, we speak with Judah Levine—legendary NIST physicist and one of the key architects of modern …
Shownotes
Why is syncing atomic clocks still one of the hardest problems in physics and engineering?
In this episode, we speak with Judah Levine—legendary NIST physicist and one of the key architects of modern timekeeping—about the invisible systems that hold the digital world together. Levine explains why synchronizing atomic clocks across the planet is far more complex than the clocks themselves, and why seemingly simple ideas like “round-trip delay” break down in real-world media such as fiber optics and the internet.
We explore how UTC is built from hundreds of atomic clocks, the difference between keeping time and *transferring* time, and the surprising challenges introduced by asymmetric delays, chromatic dispersion, and environmental noise. Levine walks us through the evolution of cesium clocks, the rise of optical clocks, and the technologies that make GPS, finance, power grids, and global communication possible.
Along the way, we discuss the history of time synchronization, from railroad schedules to radio frequencies to modern satellite systems; the ongoing debate over leap seconds; and why the future of precision timing depends not just on better clocks, but on better *engineering* to deliver those clocks’ performance to the real world.
Whether you’re curious about atomic clocks, relativity, fiber optics, GPS, the structure of time itself, or the hidden physics behind everyday technology, this conversation offers a rare look at how science, engineering, and careful statistical thinking keep modern civilization in sync—down to the nanosecond.
Follow us for more technical interviews with the world’s greatest scientists:
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Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 – Intro
01:03 – What is UTC?
05:50 – Timekeeping for Satellites
07:08 – How Radio Created Better Clocks
18:32 – From Astronomy to Atoms
25:25 – Why are there 24 Hours in a Day?
29:55 – Why Synchronizing Clocks is so Hard
47:09 – How did Judah get into Clocks?
53:29 – Is UTC Vulnerable to Hackers?
1:06:41 – Cesium vs Optical Atomic Clocks
1:11:23 – How Cesium Clocks Work
1:23:35 – Why Cesium Clocks are Imperfect
1:26:17 – Judah’s 3 Year Experiment
1:29:30 – Statistics with Clocks
1:33:40 – Is Time Real?
1:36:29 – Is the Universe Slowing Down?
1:40:29 – Atomic Time and General Relativity
1:42:17 – What’s Left for Atomic Clocks?
1:54:34 – What would Judah do with Unlimited Funding?
1:58:57 – Judah's Past in Programming
2:02:55 – Advice for Young ScientistsKapitel
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Can We Predict History Like the Weather? | Peter Turchin on Cliodynamics
Why do civilizations rise, prosper, and then collapse? Here's what the math tells us.In this episode, we sit down with Peter Turchin, complexity scientist and founder of the field of cliodynamics, which uses …
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Why do civilizations rise, prosper, and then collapse? Here's what the math tells us.
In this episode, we sit down with Peter Turchin, complexity scientist and founder of the field of cliodynamics, which uses data and mathematical models to study the long-term cycles of history. Turchin explains his theory of elite overproduction, how societies generate too many ambitious, educated elites competing for too few positions, and why this dynamic reliably leads to polarization, inequality, and political turmoil.
We explore how his structural-demographic theory maps the recurring “boom and bust” rhythms that have shaped civilizations from ancient Rome to modern America, the role of military competition in driving cooperation and social complexity, and how new tools—from AI-assisted historical databases to ancient DNA and LiDAR—are transforming the study of the past.
Whether you’re drawn to history, sociology, complexity science, or the fate of modern democracies, this conversation reveals how Turchin’s quantitative approach offers a new way to understand—and maybe even forecast—the forces that make societies rise and fall.
Follow us for more technical interviews with the world’s greatest scientists:
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LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Michael Dubrovsky: https://x.com/MikeDubrovsky
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Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:48 - Overproduction of Elites
10:56 - Did Models Predict the Rise of Trump?
20:43 - Is Russian History Repeating in the US?
26:48 - How Competition Stabilizes Societies
32:14 - What Data Goes into Cliodynamic Models?
38:13 - How New Technologies Shaped Archaeology
43:28 - Can Historians Build Mathematical Intuitions?
47:59 - What Questions can be Answered with Cliodynamics?
52:23 - Does the NYC Mayoral Race Fit into Turchin's Theory?
56:37 - Is Fear of China Bringing Us Together?
58:29 - Do Historians Reject Turchin’s Work?
1:00:03 - Trends in Civilizations and Outliers
1:03:29 - Calvary and the Evolution of Societies
1:10:03 - Is Evolution via Natural Selection a Suitable Analog for History?
1:15:16 - Could Turchin's Ideas Be Misinterpreted Dangerously?Kapitel
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Why Do Quantum Computers Make So Many Mistakes? | Mikhail Lukin on Quantum Error Correction
You can’t copy a qubit. So how do quantum computers remember anything?In this episode, we sit down with Mikhail Lukin, Harvard physicist and co-director of the Harvard Quantum Initiative, whose lab is building …
Shownotes
You can’t copy a qubit. So how do quantum computers remember anything?
In this episode, we sit down with Mikhail Lukin, Harvard physicist and co-director of the Harvard Quantum Initiative, whose lab is building quantum computers from arrays of individually trapped atoms. Lukin explains the paradox of quantum error correction—how you can safeguard quantum information even though it can’t be copied or measured directly—and why this breakthrough may be the key to making large-scale quantum computers possible.
We dive into the strange logic of superposition, entanglement, and “small cat states,” explore what makes quantum evolution inherently analog, and learn how Lukin’s team uses optical tweezers and Rydberg interactions to engineer stable, reconfigurable qubits—atoms literally held and moved by light.
Whether you’re fascinated by quantum mechanics, computing, Schrödinger’s cat, or the future of information, this conversation reveals how physicists are turning the weirdness of quantum physics into working technology—and why building a fault-tolerant quantum computer is one of the hardest and most exciting challenges in science today.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Michael Dubrovsky: https://x.com/MikeDubrovsky
Misha Shalaginov: https://x.com/MYShalaginov
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:32 - Fundamentals of Quantum Computers
04:09 - Transistors vs Quantum Gates
10:07 - What is Quantum Error Correction?
14:23 - State of the Art QEC
22:19 - Quantum Research Before Lukin
27:35 - Lukin’s Breakout Work
31:10 - From Quantum Optics to Quantum Computing
36:59 - Working with Neutral Atoms
48:17 - Funding Quantum Computers
50:00 - Transverse Gate Operations
58:22 - Is Quantum Computing All Hype?#quantumcomputing #quantumerrorcorrection #mikhaillukin #qubits #schrodingerscat #entanglement #superposition #quantumphysics
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We Interviewed the Winners of the Ig Nobel Prize | Ig Nobel 2025
The scientific stories behind this year's research that made people LAUGH, then THINK.Watch the 2025 Ig Nobel Ceremony here: https://youtu.be/z1cP4xKd_L4In this episode, we bring together three of this year’s …
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The scientific stories behind this year's research that made people LAUGH, then THINK.
Watch the 2025 Ig Nobel Ceremony here: https://youtu.be/z1cP4xKd_L4
In this episode, we bring together three of this year’s Ig Nobel winners whose research spans psychology, food science and human biology. You’ll hear how a team of psychologists devised a counter-intuitive way to boost a narcissist’s self-confidence; how two physicists uncovered the “mozzarella phase” of pecorino cheese while perfecting cacio e pepe; and how a group studying lactation discovered that garlic changes breast-milk’s aroma and baby behavior.
We explore the playful setups, surprising results and serious science behind each project, and how curiosity, humor and a dash of persistence turned ordinary questions into prize-winning research.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Michael Dubrovsky: https://x.com/MikeDubrovsky
Misha Shalaginov: https://x.com/MYShalaginov
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:19 - Physics Prize: Cacio e Pepe Sauce
30:40 - Pediatrics Prize: Garlic Breast Milk
44:48 - Psychology Prize: How to Boost Narcissism#ignobel2025 #cacioepepe #pastasauce #thermodynamics #psychology #dairy #pecorino
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What Science can Learn from Startups | Adam Marblestone on Focused Research Organizations
Science has stalled. And Adam Marblestone thinks he knows why.Check out the Research Gap Map here: https://www.gap-map.org/?sort=rankIn this episode, we sit down with Adam Marblestone, neuroscientist …
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Science has stalled. And Adam Marblestone thinks he knows why.
Check out the Research Gap Map here: https://www.gap-map.org/?sort=rank
In this episode, we sit down with Adam Marblestone, neuroscientist, nanotechnologist, and founder of Convergent Research, to explore how new “Focused Research Organizations” (FROs) could reignite scientific progress. From DNA “ticker-tape” neural recording to optical connectomics and Neuralink, Marblestone explains how emerging neurotechnologies reveal both the brilliance and the bottlenecks of today’s research system.
We discuss why traditional funding often fails to support ambitious, interdisciplinary projects, how FROs borrow the focus and speed of startups to build scientific infrastructure, and why projects like OpenAI, E11 Bio, and ultrasound-on-a-chip exemplify this new model. Marblestone breaks down his “Gap Map” of unsolved scientific challenges - from room-temperature superconductors to artificial ribosomes - and does the math on how tens of billions of dollars could close them.
Whether you’re fascinated by neuroscience, scientific innovation, or the future of research itself, this conversation offers a rare insider’s look at how new institutions could rebuild the engine of discovery—and why the next wave of breakthroughs might depend more on organization than on ideas.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Michael Dubrovsky: https://x.com/MikeDubrovsky
Misha Shalaginov: https://x.com/MYShalaginov
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:25 - Working with George Church
13:03 - Neuralink
22:23 - Gap Maps
31:47 - Artificial Ribosome
36:45 - What is Convergent Research?
40:03 - What are FROs?
44:16 - What Made OpenAI So Successful?
48:19 - Has AI Actually Impacted Science?
52:15 - Notable FROs
1:05:43 - Why Haven't There Been More Scientific Breakthroughs?
1:09:47 - Lithography and Chip Design
1:13:41 - We Can't Beat Insects
1:16:45 - What Separates Good FROs
1:18:40 - East vs West Coast Innovation
1:27:21 - Research into Longevity
1:33:27 - Advice for Grad Students
1:39:40 - How to Get Involved in FROs#neuroscience #molecularbiology #quantumphysics #researchfunding #startups
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What Optical Atomic Clocks Tell Us About Space-Time | Jun Ye
Times have changed. And cesium clocks can't keep up.In this episode, we sit down with Jun Ye, Joint Institute for Laboratory Astrophysics (JILA) Fellow and pioneer of optical lattice clocks, whose work has …
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Times have changed. And cesium clocks can't keep up.
In this episode, we sit down with Jun Ye, Joint Institute for Laboratory Astrophysics (JILA) Fellow and pioneer of optical lattice clocks, whose work has pushed timekeeping far beyond traditional cesium atomic clocks. Ye explains how combining ultra-stable lasers, frequency combs, and ultra-cold atoms produces clocks more than 100× more precise than today’s standards: so sensitive they can detect gravitational time dilation across the width of a human hair.
We explore how this next generation of atomic clocks may open windows onto gravitational waves, test Einstein’s relativity in new regimes, and even help build a GPS for space travel. Ye also shares his personal journey from growing up during China’s Cultural Revolution to becoming a leader in precision measurement, and what that experience taught him about resilience, mentorship, and protecting scientific inquiry.
Whether you’re curious about time, relativity, quantum physics, GPS technology, or the frontiers of precision measurement, this conversation offers a rare insider’s look at how breakthroughs in timekeeping can lead to entirely new physics.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Michael Dubrovsky: https://x.com/MikeDubrovsky
Misha Shalaginov: https://x.com/MYShalaginov
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.comTimestamps:
00:00 - Intro
01:17 - Why Haven't Optical Clocks Replaced Cesium Clocks?
10:45 - Fundamentals of Optical Atomic Clocks
17:34 - History of Atomic Clocks
30:18 - What is JILA?
35:01 - What brought Jun to JILA?
39:33 - What does it take to get a PhD in Physics?
42:40 - Jun Ye's PhD work
44:36 - Limitations of Laser Stabilization
50:38 - How Do We Make the Most Stable Lasers?
57:28 - How to Measure Laser Coherence Times
1:04:24 - Building Atomic Clocks from First Principles
1:08:59 - Jun's Notable Accomplishments
1:14:00 - Magic Frequencies for Optical Traps
1:21:04 - Can AI Improve Atomic Clocks?
1:24:00 - How Does Quantum Entanglement Affect Clocks?
1:30:29 - Development of Quantum Computers
1:34:23 - Pros and Cons of Nuclear Clocks
1:43:49 - What Would Jun Do With Unlimited Research Funding?
1:47:09 - Lessons from China's Cultural Revolution#quantumcomputing #quantumphysics #atomicclock #laser #physics #optics #astrophysics #astronomy #spacetime
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Laser Cooling and Quantum Timekeeping | Bill Phillips
How did cooling atoms with lasers revolutionize our understanding of time?In this episode, we speak with Bill Phillips, Nobel Laureate in Physics, about his groundbreaking work on laser cooling and trapping of …
Shownotes
How did cooling atoms with lasers revolutionize our understanding of time?
In this episode, we speak with Bill Phillips, Nobel Laureate in Physics, about his groundbreaking work on laser cooling and trapping of atoms: research that not only won him the Nobel Prize but also transformed modern timekeeping and technology. Phillips explains why breaking the Doppler cooling limit changed physics forever and what it means that today’s clocks can measure time differences caused by moving a device just a few millimeters in Earth’s gravity.
We discuss the history of timekeeping from sundials to atomic clocks, how Einstein’s relativity reshaped our view of time, and the serendipitous discovery of sub-Doppler cooling that opened the door to ultra-precise measurement, quantum computing, and fundamental tests of nature. Along the way, Phillips reflects on the culture of physics, the importance of mentorship, and the joy of discovery.
Whether you’re curious about time, relativity, quantum physics, GPS technology, or the frontiers of precision measurement, this conversation offers rare insight into how science, collaboration, and curiosity converge to shape the modern world.
Follow us for more technical interviews with the world’s greatest scientists:
Twitter: https://x.com/632nmPodcast
Instagram: https://www.instagram.com/632nmpodcast?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==
LinkedIn: https://www.linkedin.com/company/632nm/about/
Substack: https://632nmpodcast.substack.com/Follow our hosts!
Michael Dubrovsky: https://x.com/MikeDubrovsky
Misha Shalaginov: https://x.com/MYShalaginov
Xinghui Yin: https://x.com/XinghuiYinSubscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: [https://www.632nm.com](https://www.632nm.com/)Timestamps:
00:00 - Intro
01:43 - What is Time?
05:49 - How Did Bill Get Into Atom Cooling?
18:30 - How Do Atomic Clocks Actually Work?
31:08 - History of Atomic Clocks
37:18 - Laser Cooling for Atomic Clocks
40:49 - How To Synchronize Atomic Clocks
43:20 - How Cesium Cooling Was Developed
45:48 - Pushing Beyond the Doppler Limit
49:47 - The Beginning of Thor Labs
52:45 - The Previous Limits were Wrong
1:05:37 - How Bill Broke the Doppler Limit
1:12:22 - What is Optical Pumping?
1:20:27 - Can Atom Trapping Be Leveraged For Cold Fusion?
1:31:32 - What Makes Bill So Lucky?
1:35:25 - How Bill's Work Led to Atomic Clocks
1:41:05 - What Makes Cesium So Good For Atomic Clocks?
1:47:38 - Quantum Effects on Atomic Clocks
1:59:02 - Bose-Einstein Condensates
2:09:05 - Did Bill's Work Lead To Quantum Computing?
2:11:26 - Bill's Thoughts on the Future#billphillips #nobelprize #laser #atomicclock #dopplereffect #quantumcomputing #quantumphysics #gps #physics #boseeinsteincondensate #theoreticalphysics #relativity
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