17-659 — Generative AI for Quantum Computing and Machine Learning Software Implementations — Summer Semester 2023

Here’s the revised table with added details:

Lecture	Date	Title	Details
1	05/16	Intro & Intro to Quantum Computing	An overview of the course and an introduction to the basics of quantum computing.
2	05/18	Fundamentals of Quantum Computing Continued	Deep dive into the fundamental concepts of quantum computing: Qubits, Superposition and entanglement.
3	05/23	Introduction to AI, ML and DL	Introduction to the foundations of Artificial Intelligence, Machine Learning and Deep Learning.
4	05/25	Fundamentals of Generative Models	This lecture will cover the basics of generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
5	05/30	Fundamentals of LLMs	Introduction to Large Language Models (LLMs) focusing on LSTMs and Transformers.
6	06/01	Software Implementation of Generative AIs	This lecture will discuss software implementation of generative AI models using popular machine learning frameworks like TensorFlow and PyTorch, Keras.
7	06/06	LLM Implementations	Overview of practical implementation strategies for Large Language Models.
8	06/08	Quantum Programming Languages	This lecture will introduce common quantum programming languages, like Qiskit, Q#, and Cirq, and how they are used in implementing quantum algorithms.
9	06/13	Quantum Generative Models	This lecture will introduce quantum generative models like Quantum GANs (qGANs) and Quantum Variational Autoencoders (qVAEs) and how they can be implemented in quantum computing platforms.
10	06/15	Langchain	Introduction to Langchain, a specific application of Large Language Models.
11	06/20	LLM Data Preparation and Finetuning	Discussion on how to prepare and fine-tune data for Large Language Models.
12	06/22	LLMs in Machine Learning and Quantum Computing	A look at how Large Language Models are applied in both Machine Learning and Quantum Computing, with a focus on software aspects.
13	06/27	OPEN - Guest Lecture	A guest lecturer (TBA) will present on a relevant topic in the field of Quantum Computing or Machine Learning.