Scott Aaronson: Quantum Computing | Lex Fridman Podcast #72
Lex Fridman (host), Scott Aaronson (guest), Narrator
In this episode of Lex Fridman Podcast, featuring Lex Fridman and Scott Aaronson, Scott Aaronson: Quantum Computing | Lex Fridman Podcast #72 explores scott Aaronson Demystifies Quantum Computing, Supremacy, and Real-World Impact Scott Aaronson and Lex Fridman explore why big philosophical questions matter to working scientists and how math and physics make progress by reframing them into tractable sub-questions (“Q‑primes”).
Scott Aaronson Demystifies Quantum Computing, Supremacy, and Real-World Impact
Scott Aaronson and Lex Fridman explore why big philosophical questions matter to working scientists and how math and physics make progress by reframing them into tractable sub-questions (“Q‑primes”).
Aaronson then gives an accessible but technically grounded tour of quantum mechanics as a generalization of probability, introducing amplitudes, interference, qubits, decoherence, and quantum error correction.
They discuss the current “noisy intermediate-scale quantum” (NISQ) era, Google’s quantum supremacy experiment, what supremacy does and does not prove, and why breaking today’s cryptography is still far off.
The conversation closes on realistic applications (especially quantum simulation for chemistry and materials), common hype and charlatanism in the field, and Aaronson’s personal views on meaning, purpose, and scientific progress.
Key Takeaways
Use ‘Q‑prime’ questions to make philosophical riddles scientifically tractable.
Aaronson argues that progress on big questions (free will, consciousness, machine intelligence) usually comes from carving off precise, answerable sub-questions—like how well physical laws allow us to predict human behavior—rather than attacking the metaphysical question head-on.
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Quantum mechanics is best viewed as modified probability with complex amplitudes.
Instead of just nonnegative probabilities, quantum states assign complex amplitudes to possibilities; when these amplitudes evolve and interfere (constructively or destructively), we get counterintuitive phenomena like the double-slit experiment and the power behind quantum computation.
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The power of quantum computing comes from choreographing interference, not ‘trying all answers in parallel.’
A quantum computer can put exponentially many potential answers into superposition, but naïve measurement yields only a random one; useful algorithms carefully arrange interference so that wrong answers cancel out while right answers’ amplitudes reinforce.
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Decoherence is the central engineering obstacle to scalable quantum computers.
Any unwanted interaction with the environment effectively ‘measures’ qubits and destroys superposition, so practical quantum hardware must balance isolating qubits from the universe while still controlling and coupling them precisely.
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Quantum error correction enables reliability from unreliable qubits, but at huge overhead.
Theory shows that if physical error rates are below a threshold, logical qubits can be encoded across many physical qubits to suppress errors; however, current schemes would require millions of high-fidelity physical qubits to do tasks like breaking RSA, far beyond today’s 50‑ish‑qubit devices.
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Google’s ‘quantum supremacy’ is a milestone, not a practical revolution.
Their 53‑qubit device sampled from a distribution that appears infeasible for classical supercomputers to simulate in reasonable time, but the task itself is artificial; it mostly demonstrates that controllable quantum states with 2^53 amplitudes can outperform classical hardware on a carefully chosen benchmark.
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Realistic near-term value likely lies in quantum simulation, not code-breaking or generic AI.
Aaronson emphasizes that simulating quantum systems (chemistry, materials, catalysts like the Haber–Bosch process) is the most promising major application, potentially even with 100–200 qubits, whereas breaking cryptography and big quantum machine-learning speedups clearly demand much larger, error-corrected machines.
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Notable Quotes
“The entire trick with quantum computing is that you try to choreograph a pattern of interference of amplitudes.”
— Scott Aaronson
“Anything that quantum computers can do can also be done by classical computers, albeit exponentially slower in some cases.”
— Scott Aaronson
“Quantum supremacy is already enough by itself to refute the skeptics who said a quantum computer will never outperform a classical computer for anything.”
— Scott Aaronson
“We know how to do in theoretical computer science... we don’t know how to prove that most of the problems we care about are hard, but we know how to pass the blame to someone else.”
— Scott Aaronson
“Again and again, I’ve undergone the humbling experience of first lamenting how badly something sucks, then only much later having the crucial insight that its not sucking wouldn’t have been a Nash equilibrium.”
— Scott Aaronson, as quoted by Lex Fridman
Questions Answered in This Episode
If we ever built a highly accurate ‘prediction machine’ for human behavior, how would that change our lived experience of free will and moral responsibility?
Scott Aaronson and Lex Fridman explore why big philosophical questions matter to working scientists and how math and physics make progress by reframing them into tractable sub-questions (“Q‑primes”).
Get the full analysis with uListen AI
What specific breakthroughs in error correction or hardware design would most radically reduce the qubit overhead needed for practical, fault-tolerant quantum computers?
Aaronson then gives an accessible but technically grounded tour of quantum mechanics as a generalization of probability, introducing amplitudes, interference, qubits, decoherence, and quantum error correction.
Get the full analysis with uListen AI
How can we better separate genuine quantum speedups from hype in areas like optimization and machine learning, where classical algorithms are also rapidly improving?
They discuss the current “noisy intermediate-scale quantum” (NISQ) era, Google’s quantum supremacy experiment, what supremacy does and does not prove, and why breaking today’s cryptography is still far off.
Get the full analysis with uListen AI
Once we achieve useful quantum simulations in chemistry or materials, what kinds of industries or global challenges (e.g., energy, fertilizer, climate tech) are most likely to be disrupted first?
The conversation closes on realistic applications (especially quantum simulation for chemistry and materials), common hype and charlatanism in the field, and Aaronson’s personal views on meaning, purpose, and scientific progress.
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In practice, how should governments and industry balance investment in quantum computing with parallel efforts in post-quantum cryptography to avoid a future security cliff?
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Transcript Preview
The following is a conversation with Scott Aaronson, a professor at UT Austin, director of its Quantum Information Center, and previously a professor at MIT. His research interests center around the capabilities and limits of quantum computers and computational complexity theory more generally. He is an excellent writer and one of my favorite communicators of computer science in the world. We only had about an hour and a half for this conversation, so I decided to focus on quantum computing, but I can see us talking again in the future on this podcast at some point about computational complexity theory and all the complexity classes that Scott catalogs in his amazing Complexity Zoo Wiki. As a quick aside, based on questions and comments I've received, my goal with these conversations is to try to be in the background, without ego, and do three things. One, let the guest shine and try to discover together the most beautiful insights in their work and in their mind. Two, try to play devil's advocate just enough to provide a creative tension in exploring ideas through conversation. And three, to ask very basic questions about terminology, about concepts, about ideas. Many of the topics we talk about in the podcast, I've been studying for years as a grad student, as a researcher, and generally as a curious human who loves to read. But frankly, I see myself in these conversations as the main character from one of my favorite novels by Dostoevsky called The Idiot. I enjoy playing dumb. Clearly, it comes naturally. But the basic questions don't come from my ignorance of the subject, but from an instinct that the fundamentals are simple, and if we linger on them from almost a naive perspective, we can draw an insightful thread from computer science to neuroscience, to physics, to philosophy, and to artificial intelligence. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter @LexFridman, spelled F-R-I-D-M-A-N. As usual, I'll do one or two minutes of ads now and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. Quick summary of the ads. Two supporters today. First, get Cash App and use the code LEXPODCAST. Second, listen to the Tech Meme Ride Home Podcast for tech news. Search "Ride Home," two words, in your podcast app. This show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LEXPODCAST. Cash App lets you send money to friends, buy Bitcoin, and invest in the stock market with as little as $1. Broker services are provided by Cash App Investing, a subsidiary of Square and member SIPC. Since Cash App does fractional share trading, let me mention that the order execution algorithm that works behind the scenes to create the abstraction of fractional orders is an algorithmic marvel. So big props to the Cash App engineers for solving a hard problem that, in the end, provides an easy interface that takes a step up to the next layer of abstraction over the stock market, making trading more accessible for new investors and diversification much easier. So again, if you get Cash App from the App Store or Google Play and use the code LEXPODCAST, you'll get $10 and Cash App will also donate $10 to FIRST, one of my favorite organizations that is helping to advance robotics and STEM education for young people around the world. This episode is also supported by the Tech Meme Ride Home Podcast. It's a technology podcast I've been listening to for a while and really enjoying. It goes straight to the point, gives you the tech news you need to know, and provides minimal but essential context. It's released every day by 5:00 PM Eastern and is only about 15 to 20 minutes long. For fun, I like building apps on smartphones, mostly on Android, so I'm always a little curious about new flagship phones that come out. I saw that Samsung announced the new Galaxy S20, and of course, right away, Tech Meme Ride Home has a new episode that summarizes all that I needed to know about this new device. They've also started to do weekend bonus episodes with interviews of people like AOL founder Steve Case on investing and Gary Marcus on AI, who I have also interviewed on this podcast. You can find the Tech Meme Ride Home Podcast if you search your podcast app for "Ride Home," two words. Then subscribe, enjoy, and keep up to date with the latest tech news. And now here's my conversation with Scott Aaronson. I sometimes get criticism from a listener here and there that while having a conversation with a world-class mathematician, physicist, neurobiologist, aerospace engineer, or a theoretical computer scientist like yourself, I waste time by asking philosophical questions about free will, consciousness, mortality, love, nature of truth, super intelligence, whether time travel is possible, whether space-time is emergent or fundamental. Uh, even the crazier questions like whether aliens exist, what their language might look like, what their math might look like, whether math is invented or discovered, and of course whether we live in a simulation or not. So I try to- Out with it. (laughs) Out with it. I try to dance back and forth from the deep technical- Mm-hmm. ... to the philosophical. So I've, I've done that quite a bit. Mm-hmm. So you're a world-class computer scientist, and yet you've written about this very point that philosophy is important for experts in, uh, any technical discipline, though they somehow seem to avoid this.So, I thought it'd be really interesting to talk to you about this point. Why should we computer scientists, mathematicians, physicists care about philosophy, do you think?
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