**Faculty Mentor: Ying Mao (Fordham)**

# Description

In the post Moore’s Law era, the proximity to the physical bound of semiconductor fabrication along with the increasing size of datasets raises the discussion on the future of deep learning and its limitations, which would affect people’s daily routine. For example, UberEats relies on deep learning models to allocate delivery services nearby. Tesla Autopilot utilizes deep learning algorithms to recognize objects near the vehicle. Slowing down the learning process will not only delay the daily food delivery, but also, potentially, cost lives.

In parallel, the fast development of quantum computing has pushed classical designs to the quantum stage, which breaks the physical bound for deep learning applications. For instance, Google demonstrated quantum Supremacy using a 53-qubit quantum computer, where it spent 200 seconds to complete a task that would cost 10,000 years on the world’s largest classical computer. Due to the endless potential, quantum-based deep learning architectures attract increasing attention in both industry and academia, in hopes that certain systems might offer a quantum speedup.

This project aims to develop a novel quantum-based learning architecture and workflow to improve the classical algorithms.

# Participant Background

This project is appropriate for participants who python programming background and basic mathematical skills (e.g. Matrix multiplication).

# Objectives and Learning Goals

The participants in this project will achieve the following:

- Learn about basic quantum operations with Qiskit, a python based quantum programming framework.
- Study the Tensorflow platform and Tensorflow Quantum.
- Build a quantum based classifier.