Introduction to Quantum Deep Learning

Understanding Quantum Variational Circuits

Abhilash Majumder

Deep Learning Generative Adversarial Networks Machine-Learning Natural Language Processing python

See in schedule: Wed, Jul 28, 14:15-15:00 CEST (45 min) Download/View Slides

Introduction to Quantum Deep Learning
Abstract
The aim of the lightning talk is to shed light into the field of Quantum computation in the field of Deep Learning. Qubits , which form the fundamental units of quantum computing can be used to create quantum variational circuits which can be placed over traditional deep learning networks to create hybrid quantum-deep learning models. These models not only rely on the gradient convergence properties of general backpropagation technique, but also on the final probabilistic states of the Qubits. Essentially there has been quite a development to optimize the gradient convergence of these hybrid models with the help of Fischer approximation and Natural Gradient Descent.The talk would focus on the importance of Quantum Variational Deep Learning Circuits and how they provide an advantage over traditional Autograd based Circuits. The application of Quantum Variational circuits in the field of Reinforcement Learning as well as NLP would be one of the main points of the talk. There has been sufficient development in the field of quantum computing and this talk aims to throw light on how to exploit the probabilistic states of Qubits to enhance deep learning models.

Topics:
Introduction to Quantum Computing and Qubit system
Quantum Variational Circuits
Creating Hybrid Circuits (Classical-Quantum-Classical etc.)
Realizing Performance of Hybrid Circuits
Applications in the field of Quantum RL and Quantum NLP (research)
Democratizing adoption of Quantum Circuits over traditional deep learning circuits
Resources
Resources (slides, repositories) would be added in course of time.
Resources: https://drive.google.com/file/d/1gtCc8JViacFhlV3-XfKlGruh_OqI0ZIa/view?usp=sharing

Type: Talk (45 mins); Python level: Intermediate; Domain level: Intermediate