Scott Aaronson is 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. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast". This episode is also supported by the Techmeme Ride Home podcast. Get it on Apple Podcasts, on its website, or find it by searching "Ride Home" in your podcast app. Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 05:07 - Role of philosophy in science 29:27 - What is a quantum computer? 41:12 - Quantum decoherence (noise in quantum information) 49:22 - Quantum computer engineering challenges 51:00 - Moore's Law 56:33 - Quantum supremacy 1:12:18 - Using quantum computers to break cryptography 1:17:11 - Practical application of quantum computers 1:22:18 - Quantum machine learning, questionable claims, and cautious optimism 1:30:53 - Meaning of life
In this episode, Lex Friedman talks to renowned computer scientist Scott Aaronson about quantum computing and computational complexity theory. The conversation covers the basics of quantum computing, the differences between classical and quantum computers, and the potential impact of quantum computing on cryptography and other fields.
In this episode, scientist and philosopher David Chalmers discusses the relationship between scientific and philosophical questions, the exploration of consciousness, and the possibility of living in a simulation.
A discussion around the accuracy required to make predictions of an individual's behavior without fundamentally altering their brain, and the potential for chaos in the behavior of sodium ion channels to impede predictive accuracy.
The role of consciousness in the debate on AI's demonstration of intelligence, free will, and consciousness is questioned, and its transformation in a world dominated by human-level AIs.
In this podcast, the speaker explains the concept of the 'bit' and how it's essential for quantum computing to exploit phenomena like superposition, amplitudes, and interference to solve problems faster than we know how to solve them otherwise.
Decoherence is the unwanted interaction between qubits and the external environment when building a quantum computer. However, to build a reliable quantum computer with an arbitrary number of qubits, they do not have to be perfectly isolated from the external environment.
We are finally at the point where noisy non-error corrected quantum computers can do something that is hard for classical computers to simulate, which marks the beginning of a race to try and create something beyond what a classical computer can do that is also useful, with Google, IBM, startups, and research labs all competing over the next decade.
Experts discuss the economic pressure that is driving the development of quantum computing as a way to achieve quantum supremacy, where a quantum computer can perform a well-defined task much faster than any classical computer available.
The central word in computer science is the efficient scaling, which aims to have an algorithm that uses an amount of time that scales only like the size of the input raised to some polynomial power. Quantum computers are expected to be faster in solving real-world problems with a polynomial scale, while classical computers would have an exponential scale.
The number of qubits necessary for a quantum computer to outpace classical computers will need to be sufficient to verify the results, but computational complexity cannot prove definitively that there could not be possibilities for fast classical algorithms for simulating experiments.
The biggest practical application of quantum computing is to simulate quantum mechanics in order to design new materials, drugs, solar cells and superconductors. Within the next decade, it’s unlikely that the best shot we have in quantum computing is using Shor's algorithm to break cryptography due to the amount of error correction required.
Even with scalable and fault-tolerant quantum computers, there are only modest speed ups for optimization, machine learning, and AI problems, making classical algorithms just as important to consider.
In this podcast episode, the speaker talks about his priorities in life, including trying to make the world better in some small ways and valuing his relationships with family, friends, and students.
Lex Fridman talks to Scott Aaronson.