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.com

Timestamps:
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

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
Michael Dubrovsky: https://x.com/MikeDubrovsky
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](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

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:
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!
Mikhail Shalaginov: https://x.com/MYShalaginov
Michael Dubrovsky: https://x.com/MikeDubrovsky
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](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