## Summary

AI is currently dominated by Deep Learning and Large Language Models, but there is other very interesting research that has the potential to have great impact on our lives in the future; one of them being Quantum Machine Learning (QML)

Today on the podcast, I am talking to Christa Zoufal, Quantum Machine Learning Researcher at IBM.

Christa will explain how Quantum Computing (QC) and Quantum Machine Learning relates to classical computing (CC).

We will discuss Qbit's, the fundamental information storage and processing unit of quantum computers that in comparison with bits in a classical computers, not only store a single state of zero or one, but a superposition of the two.

Christa will explain how Quantum algorithms are created by building quantum circuits that operate on those qbits. We will talk about the challenges when operating on those physical qbits that need to be kept at very low temperatures in order to operate without noise perturbing their states, and why one combines multiple physical qbits to a single logical qbit in order to perform error correction.

How current quantum circuits are limited in the number of logical qbits they can contain and the number of operations; in the sense of the size of an algorithm, they can perform.

Why these properties make QC is the wrong choice for BigData applications, but QM could in the future be used for applications where very little data is available and quantum algorithms exist that outperform their known classical counterparts.

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## TOC

00:00:00 Beginning

00:03:21 Guest Introduction

00:07:08 Quantum Computer (QC) & Quantum Machine Learning (QML)

00:14:50 Difference between classical computing and QC

00:24:59 What kind of programs one can run on a QC

00:38:43 Examples of Quantum Machine Learning

00:49:25 Current applications of QC

## References

Christa Zoufal: https://www.linkedin.com/in/christa-zoufal/

IBM Quantum Learning: https://learning.quantum.ibm.com/course/basics-of-quantum-information

Quantum machine learning course: https://github.com/Qiskit/textbook/tree/main/notebooks/quantum-machine-learning

Scott Aaronson @ Lex Podcast https://www.youtube.com/watch?v=nK9pzRevsHQ

State of the Art Bottlenecks: https://arxiv.org/abs/2312.09121