Quantum Supremacy to Generative AI and Back with Scott Aaronson

The New Quantum Era

Description: Welcome to another episode of The New Quantum Era Podcast hosted by Kevin Rowney and Sebastian Hassinger. Today, they are joined by Scott Aaronson, who is a leading authority in the space of Quantum Computing, a fascinating person with a long list of relevant achievements. Scott is also the author of an outstanding blog called Shtetl-Optimize and a book named Quantum Computing Since Democritus.

Scott helped design Google Quantum Supremacy, but his work exceeds it; he is involved in Complexity Theory and Computer Science and is just extremely good at connecting, explaining, and digging deeper into concepts.

Key Takeaways:

[3:38] How did Scott get into quantum computing?

[11:35] Scott talks about the moment when the question arose: Does nature work this way?

[14:28] Scott shares when he realized he wanted to dig deeper into Quantum Computing.

[15:56] Scott remembers when he proved the limitation of quantum algorithms for a variation of Grover's search problem.

[18:43] Scott realized that his competitive advantage was the ability to explain how things work.

[20:01] Scott explains the collision problem.

[21:33] Scott defines the birthday paradox.

[23:24] Scott discusses the dividing line between serious and non-serious quantum computing research.

[24:11] What's Scott’s relative level of faith and optimism that the areas of topological quantum computing and measurement-based quantum computation are going to produce?

[28:33] Scott talks about what he thinks will be the source of the first practical quantum speed-up.

[31:55] Scott didn’t imagine that being a complexity theorist would become exponential.

[36:14] Is Scott optimistic about quantum walks?

[40:11] Has Scott returned to his machine learning and AI roots but is now trying to explain the concepts?

[42:03] Scott was asked: ‘What is it going to take to get you to stop wasting your life on quantum computing?’

[44:50] Scott talks about the future need to prevent AI misuse. and his role in Open AI

[47:41] Scott emphasizes the need for an external source that can point out your errors.

[50:13] Scott shares his thoughts about the possible risks and misuses of GPT.

[51:40] Scott made GPT to take a Quantum Computing exam; what did surprise him about the answers? It did much better on conceptual questions than on calculation questions

[55:55] What kind of validation will we be able to give GPT?

[56:22] Scott explains how RLHF (Reinforced Learning from Human Feedback) works.

[59:28] Does Scott feel that there's room for optimism that educators can have a decent tool to hunt down this kind of plagiarism?

[1:02:08] Is there anything that Scott is excited about seeing implemented on 1000 gate-based qubits with a decent amount of error mitigation?

[1:04:05] Scott shares his interest in designing better quantum supremacy experiments.

[1:07:43] Could these quantum supremacy experiments (based on random circuit sampling) already deliver a scalable advantage?

[1:10:58] Kevin and Sebastian share the highlights of a fun and enlightening conversation with Scott Aaronson.

Mentioned in this episode:

Visit The New Quantum Era Podcast

Check Shtetl-Optimize

Quantum Computing Since Democritus, Scott Aaronson

Learn more about the Adiabatic Algorithm result by Hastings and the Quantum Walk Algorithm result by Childs et Al.

Tweetables and Quotes:

“The dividing line between serious and nonserious quantum computing research is, are you asking the question of, ‘Can you actually be the best that a classical computer could do at the same desk? “ — Scott Aaronson

“My first big result in quantum computing that got me into the field was to prove that Prasad Hoyer tap algorithm for the collision problem was optimal.” — Scott Aaronson

“ Quantum Walks are a way of achieving Grover type speed ups at a wider range of problems than you would have expected.” — Scott Aaronson

“AI safety is now a subject where you can get feedback.” — Scott Aaronson

“We don't have any theorems that would explain the recent successes of deep learning, the best way we can explain why is that none of the theorems rule it out.” — Scott Aaronson