632nm

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.Follow us for more technical interviews with the world’s greatest scientists:Facebook: https://www.facebook.com/632nmPodcastOfficialTwitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro00:58 - Protecting Quantum Information09:28 - Reinforcement Learning for QEC13:47 - Qubit Calibration19:10 - Where Do Noise and Error Come From?21:13 - Scalability of RL Methods25:28 - Google's AlphaQubit31:54 - RL vs Human Calibration33:26 - Settling on RL vs Other ML Approaches39:57 - Discovery in the Age of AI44:59 - Collaboration Between Google Deepmind and Quantum AI46:17 - Combining Quantum and AI48:35 - Will Human Input Become Obsolete?55:00 - Controlling Bosonic Codes1:05:17 - Break Even Point1:121:51 - Can Syndrome Extraction Be Fault Tolerant?1:15:41 - Building Gates1:20:22 - Surface Codes1:27:38 - Scaling Superconducting Qubits1:30:22 - How Quantum Decoders Work1:35:47 - Model Sizes For Larger Quantum Computers1:37:48 - What Problems Could Be Solved With Unlimited Resources?

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 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.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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://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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro01:16 - Platonic Space03:55 - Biology in the Absence of Evolution21:11 - Extracting Knowledge from Platonic Space37:04 - Development and Loss Functions50:09 - Investigating Layers of Control55:35 - Is Computer Science Misleading Our Understanding of Minds?58:14 - Bioelectric Patterns and Drug Discovery1:04:07 - How to Build a Predictive Developmental Model1:13:20 - Levin’s Top Books, Thoughts on Collective Existence1:18:00 - Questions that Keep Michael Up at Night1:22:32 - How Does Levin Interact with AI?#biology #evolution #computerscience #machinelearning #intelligence

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" 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.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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://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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro01:10 - mRNA Vaccine Basics10:20 - Costs of mRNA Vaccine Development15:59 - Lessons from Building GitLab19:19 - Standard of Care for Cancer24:50 - Sid’s Life Story and GitLab30:49 - Going Founder Mode Against Cancer42:03 - Selecting Experimental Treatments50:38 - How Did Sid Know Treatments Were Working?58:08 - AI for Treatment Assistance1:02:54 - Making Personalized Treatments Affordable1:08:17 - Where are the Levers for Improving Drug Development?1:10:54 - Most Promising Areas of Drug Development1:17:14 - The Happy Accident of Sid’s Biotech Investments1:21:07 - Reflecting on Building GitLab1:22:46 - How Does Sid Start So Many Companies?1:26:02 - Finding the Right People for Early Companies1:34:19 - Will the Bahamas be the Next Singapore?1:38:42 - Advice for Young Scientists#cancer #biology #immunology #cancerresearch #gitlab

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.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro01:06 - Secrecy of Fabrication03:24 - 2 Qubit Gate with Transmons05:18 - New Knowledge of Superconducting Quantum Computers08:53 - What are Transmons?35:56 - Lessons from Failure38:05 - The Role of Theory in Martinis’ Work44:51 - Two Level States48:15 - Engineering Tricks in Superconducting Quantum Computers53:32 - Metrics for Quantum Success1:00:11 - Scaling Quantum Computers1:08:08 - Identifying Sources of Error1:16:15 - Quantum Supremacy Experiment1:25:45 - What If Quantum Mechanics Failed?1:33:10 - Is Quantum Supremacy Holding Up?1:34:42 - Lift-off Fabrication for Superconducting Quantum Computers1:41:59 - Quantum Flexibility vs Foundries1:43:40 - Connecting Distant Qubits1:47:41 - Codesign for Fault-Tolerance1:49:16 - Martinis’ Nobel Prize2:01:55 - Advice for Young Scientists#quantumcomputing #quantumphysics #superconductor #nobelprize #fabrication

What does a cell actually look like when you can see its molecules in action?In this episode, we speak with Nobel Prize-winning scientist Eric Betzig, whose pioneering work in super-resolution microscopy transformed our ability to see inside living cells. Betzig recounts his decades-long effort to overcome the diffraction limit of light microscopy, from his early work in near-field microscopy to the development of PALM and his eventual focus on watching biological processes unfold in living cells.We explore why the familiar picture of the cell in biology textbooks may be fundamentally misleading. Much of cell biology has been built by combining observations from biochemistry, molecular biology, and structural biology to construct models of how molecules interact. But, as Betzig explains, we have historically had very little direct information about the spatial organization and dynamics of these molecules inside a living cell. When he and his colleagues used single-molecule microscopy to watch transcription factors in real time, they found that proteins believed to form stable complexes were instead binding to DNA for only a few seconds, forcing them to reconsider how transcription actually works.We discuss the diffraction limit, why conventional light microscopes cannot resolve structures at the scale of individual proteins, and how super-resolution microscopy made it possible to study molecular processes with unprecedented spatial and temporal resolution. Betzig also explains why imaging living cells can reveal dynamics that are invisible in fixed samples.Betzig describes his ambitious Cell Observatory project, which combines automated microscopy, large-scale biological experiments, and artificial intelligence to study the enormous complexity of living cells. Rather than trying to build a “virtual cell” from incomplete measurements, he argues that biology first needs to observe these systems at a much larger scale and turn the resulting data into genuine understanding.Finally, Betzig reflects on what microscopy has taught him about scientific discovery, why the cell may be the most complex form of matter we know, and why better ways of observing life could fundamentally change our understanding of biology.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro01:30 - The Diffraction Limit12:30 - Imaging Cells19:04 - Betzig's Transition from Physics to Biology32:37 - Getting Fed Up with Science34:57 - Leaving Science for the Automotive Industry55:55 - 2008 and the Fall of the Automotive Industry1:09:50 - Building a Microscope in a Living Room1:34:01 - Insights from Super-Resolution Microscopy1:47:53 - AI for Analyzing Petabytes of Data2:10:35 - Improving Microscopes2:15:27 - Nuclear Energy and Politics2:23:18 - The Magic of Bell Labs2:36:17 - Is SpaceX the New Bell Labs?#microscopy #cellbiology #superresolution #fluorescence #nobelprize

Why is controlling a quantum hardware becoming one of the biggest challenges in scaling quantum computers?In this episode, we speak with Yonatan Cohen, co-founder and CTO of Quantum Machines, a company developing advanced control systems for quantum computers. Cohen explains how quantum control sits at the interface between quantum hardware and classical computing, and why this hybrid architecture will become increasingly important as quantum processors scale.We explore how quantum computers are controlled using precise microwave signals and pulse sequences, the limitations of conventional arbitrary waveform generators, and how Quantum Machines uses FPGA-based pulse processing units to generate waveforms in real time. We also discuss why low-latency classical processing and real-time feedback are essential for calibrating quantum processors, correcting errors, and implementing increasingly complex quantum algorithms. Cohen explains what changes when moving from small quantum processors to thousands or millions of qubits, including the challenges of data movement, power consumption, control-channel density, and latency. We also discuss quantum error correction, feed-forward operations, hybrid quantum-classical architectures, and the role of CPUs, GPUs, and FPGAs in stabilizing large-scale quantum systems. We also discuss the origins of Quantum Machines, the company's approach to quantum control, and why building scalable quantum computers requires much more than simply increasing the number of qubits.Whether you're interested in quantum computing, quantum control, quantum error correction, computer architecture, FPGA technology, or the future of fault-tolerant quantum computers, this episode provides a deep technical look at the control infrastructure required to make large-scale quantum computing possible.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro and Reads02:37 - Quantum Machines and Hybrid Architecture06:04 - Why Do Classical Computers Need Quantum Processors?10:02 - State of the Art Controllers20:02 - Meeting Itamar24:01 - FPGAs for Quantum29:29 - Repurposing FPGAs35:22 - Remote Direct Memory Access (RDMA)41:29 - What If We Had Perfect Controllers?43:26 - Adaptive and Embedded Calibrations47:15 - Picks and Shovels of Quantum Computing49:14 - Progress in Different Qubits53:25 - Keeping Up with Quantum News and Research56:51 - Core Advantages of Quantum Machines1:00:06 - Managing Larger Teams1:02:12 - Discovering New Physics with Quantum Machines1:14:31 - Reinforcement Models in Quantum1:16:45 - Yonatan’s Intro to Quantum Computing1:21:24 - Realtime Correction vs Post Processing1:32:10 - Channel Numbers and Interfering Signals1:39:38 - Connecting Multiple Modules1:46:46 - Early Believers in Quantum Machines1:51:05 - What Would Yonatan Do With Unlimited Resources?#quantumcomputing #quantumphysics #computerscience #fpga #coding

How did we go from knowing almost nothing about genes to sequencing the entire human genome?In this episode, we speak with Nobel Prize-winning molecular biologist Walter Gilbert, whose discoveries helped lay the foundation for modern genomics. Gilbert recounts his remarkable journey from theoretical physics into biology, where he helped discover messenger RNA, uncovered the molecular mechanisms of gene regulation, invented one of the first practical methods for sequencing DNA, and later co-founded Biogen, one of the world's first biotechnology companies.We explore the race to understand how genes work, the search for the elusive lac repressor, how a chance experiment led to the invention of DNA sequencing, and why Gilbert believed decades in advance that sequencing the human genome would transform biology into an information science. He explains the origins of the Human Genome Project, the rise of computational biology, and why today's era of AI-driven genomics was already visible in the earliest DNA sequence databases.We also discuss the RNA World hypothesis, how life may have begun with self-replicating RNA molecules, the evolution of gene regulation, exon shuffling, the origins of protein domains, recombinant DNA technology, the birth of the biotechnology industry through Biogen, and how scientific revolutions often emerge from unexpected experiments.Finally, Gilbert reflects on creativity in both science and art, explaining why, after a lifetime of pioneering discoveries, he left the laboratory to pursue digital abstract art.Whether you're interested in DNA sequencing, the Human Genome Project, molecular biology, biotechnology, computational biology, the origin of life, RNA World, gene regulation, genomics, or the history of modern biology, this episode offers a firsthand account from one of the scientists who helped build the field.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro02:34 - Jim Watson and Beginning Biology06:58 - Lac Repressor15:27 - Picking Good Problems17:57 - Developing the First Generation of Sequencing31:09 - The Birth of the Human Genome Project40:00 - Origins of Life43:35 - RNA World Hypothesis54:45 - Experiments vs Theory in Biology59:07 - Inspiration from Other Discoveries1:12:08 - Starting Biogen1:22:24 - Balancing Industry and Academia1:27:45 - Advice for CEOs1:32:48 - Perspectives on Art and Science1:37:49 - Walter’s Journey through Art1:44:55 - Walter’s Artistic Process and Inspirations1:50:32 - The Role of Theory in Biology1:55:51 - Frontiers and Guidance in Science1:59:37 - Should Everyone Get Sequenced?#biology #dnasequencing #originsoflife #genetics #humangenomeproject

How do bacteria power one of the most sophisticated molecular machines in nature?In this episode, we speak with Dr. Michael Manson, one of the pioneers of bacterial motility research, whose nearly 50-year career has helped uncover how the bacterial flagellar motor works. From the first experiments proving that bacterial flagella rotate to the latest breakthroughs in cryo-EM and single-molecule biology, Manson tells the story of how scientists finally solved the mechanism behind a real working biological motor.We explore how bacteria move through chemotaxis using a biased random walk, why E. coli alternates between running and tumbling, and how individual molecules can control the direction of a spinning flagellum. Manson explains the experiments that showed proton motive force powers the flagellar motor, how the motor’s rotor and stator generate torque, why it can reverse direction almost instantly, and how bacteria adapt to changing environments by dynamically adjusting their molecular machinery.We also discuss ATP synthase, proton gradients, molecular motors, bacterial genetics, cryo-electron microscopy, ion channels, self-assembling protein complexes, nanomachines, and the history of the discoveries that transformed modern microbiology.Whether you’re interested in the bacterial flagellar motor, molecular biology, biophysics, microbiology, ATP synthase, chemotaxis, molecular machines, or the fundamental physics of life, this week we go deep into one of biology’s most remarkable inventions.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro and Reads02:42 - Biased Random Walk10:27 - Manson's Work with Howard Berg13:24 - Proton Motive Force and Flagellum29:07 - Rotors and Stators of Flagella37:20 - Mot Proteins57:34 - CheY and Changing Direction1:11:48 - Biology and Intelligent Design1:26:52 - Reversing Proton Flow1:29:59 - Life at Low Reynolds Number1:39:15 - Mysteries in the 90s and 2000s1:48:34 - Applications of Understanding the Nanomotor1:58:33 - Flagellar Motor Crash Course2:01:46 - Bacterial Learning and Adaptation2:05:56 - Giving Up on Birds2:14:09 - Caltech2:21:38 - Advice for Young Scientists2:30:02 - Origins of Life2:31:19 - What's Left for the Flagellar Motor?PART 2:2:33:33 - Building the Nanomotor2:39:29 - Other Types of Flagella2:47:06 - MotA and MotB3:07:17 - Reusing Motors Across Biology3:10:13 - Benefits of Being Small3:12:34 - CheY and Changing Direction3:19:39 - How Physics Shapes Evolution

Why are so many companies betting on neutral atoms to build the first useful quantum computers?In this episode, we speak with Mark Saffman, professor at the University of Wisconsin–Madison and one of the pioneers of neutral atom quantum computing. Over the past two decades, Saffman has helped transform Rydberg atoms from a theoretical idea into one of the leading architectures for scalable, fault-tolerant quantum computing.We explore the physics of optical tweezers and Rydberg blockade, how neutral atoms perform quantum logic and create entanglement, and why this platform offers unique advantages in connectivity and scalability. Saffman also discusses the engineering challenges of improving gate fidelity, implementing quantum error correction, and scaling from small laboratory experiments to processors containing millions of qubits.We also discuss the origins of companies like Infleqtion, the rapid growth of the neutral atom ecosystem, and what it will take for quantum computers to solve meaningful scientific and industrial problems.Whether you're interested in quantum computing, atomic physics, quantum error correction, computer architecture, or the future of information processing, this episode provides a deep technical look at one of the most promising paths toward practical quantum computers.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro and Reads02:45 - Neutral Atoms vs Superconductors and Ions07:30 - Rydberg Atoms12:49 - Practical Considerations for Rydberg Atoms19:04 - From Atomic Physics to Quantum Gates29:49 - Increasing Trap Loading38:27 - Evolution of Rydberg Gates45:05 - Limits of Rydberg Fidelity49:49 - Scaling Neutral Atom Arrays53:47 - Atomic Species and QEC1:03:38 - History of Infleqtion1:10:27 - Mark’s Outlook on the Future1:15:08 - Caltech and Peter Shor1:20:00 - Advice for Young Scientists#quantumphysics #quantumcomputing #physics #computerscience

Why are some of the world's largest technology companies betting on silicon photonics?In this episode, we speak with John Bowers, professor at UC Santa Barbara and one of the pioneers of silicon photonics, about the technologies that are transforming AI infrastructure and modern data centers. Bowers explains why moving data has become one of the central challenges in computing, how optical communication is overcoming the limitations of traditional electrical interconnects, and why light is increasingly being used to connect processors, servers, and entire data centers.We explore the origins of silicon photonics, from early optical communications research to the development of integrated photonic devices that can be manufactured using semiconductor processes. Bowers discusses the engineering challenges of combining lasers with silicon, the breakthroughs that enabled heterogeneous integration, and how decades of research helped turn silicon photonics into a commercial technology deployed at global scale.We examine the growing demands of artificial intelligence, where the movement of information between processors has become just as important as computation itself. Bowers explains why bandwidth, power consumption, and interconnect density are emerging as critical bottlenecks for AI systems, and how optical links are enabling the next generation of large-scale computing architectures.We also discuss data center networking, optical interconnects, co-packaged optics, heterogeneous integration, semiconductor manufacturing, photonic integrated circuits, telecommunications, AI hardware, and the future of warehouse-scale computing. Throughout the episode, Bowers provides an inside look at how advances in photonics are reshaping the infrastructure that powers modern computing.Whether you're interested in silicon photonics, optical communications, semiconductor engineering, computer architecture, AI hardware, data center design, networking, integrated photonics, electrical engineering, or the future of computing, this episode provides a deep technical exploration of one of the most important technologies behind the AI revolution.Follow us for more technical interviews with the world’s greatest scientists:Twitter: https://x.com/632nmPodcastInstagram: 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!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/id1751170269Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6ORWebsite: https://www.632nm.comTimestamps:00:00 - Intro01:19 - Why Data Centers Need Photonics05:28 - Bowers's Interest in Physics10:09 - Lessons From Bell Labs12:58 - Semiconductor Lasers18:31 - Teaching Entrepreneurship23:21 - Heterogeneous Integration29:40 - Why Silicon Photonics Needed Better Light Sources32:00 - Heterogeneous Integration vs Direct Growth44:04 - The Packing Problem in Photonics47:49 - Narrow Linewidth Lasers51:31 - Data Centers in Space59:19 - Lessons from the Telecom Bubble1:02:17 - Recent Breakthroughs in Photonics1:04:32 - What is a Frequency Comb?1:07:07 - Solitons and Microcombs1:14:48 - Optical Computing and AI1:19:09 - How Bowers Starts Companies1:21:56 - Was Bowers Late to Any Trends?1:22:51 - What would Bowers Build with Unlimited Resources?1:24:38 - Creating Bell Labs for AI1:26:35 - Competition, Endurance, and Personality1:30:41 - The Best Problems for Young Scientists to Tackle1:37:47 - Advice for Researchers Who Want to Keep Real Depth#photonics #datacenter #siliconphotonics #computerscience #artificialintelligence