[Done] Histopathological Cancer Detection using Hybrid Quantum Computing ERS_CRRCT23

[RamAIbot]

Project Name:

Histopathological Cancer Detection using Hybrid Quantum Computing.

Team Name:

ERS_CRRCT23

Which challenges would you like to submit your project for?

• Quantum Computing today.
• Hybrid Quantum Classical computing challenge.
• AWS Braket Challenge.

Project :

Our proposed project focuses on using the principles of quantum computing and machine learning to provide effective solutions to the healthcare industry. Specifically, we are looking to implement, and potentially improve on, the hybrid model introduced by R. Majumdar, et al. this year. The American Cancer Society projects that there will be 1.9 million new cancer diagnoses in the United States this year alone. Early detection provides the best chance for survival and a cure, allowing for treatment in the earlier stages. Thus, this solution aims to aid in patient best-outcome through this early detection system which predicts the cancer stage using the digital pathology images from the patient. Using this early detection system, patients can identify the severity of the cancer before it becomes impossible to cure.

Objectives

• Exploring the use of quantum computing in the field of machine learning and using quantum mechanics to aid machine learning in order to provide better accuracy and sensitivity.
• Using Covalent workflow to deploy the model and to provide inference for new test cases.
• Using AWS bracket as the backend to run the quantum computing model in a physical quantum computer.
• Performance analysis of various approaches.

Project Link:

https://github.com/egrace479/ERS_CRRCT23/tree/643300e095deff37a142926bf173f1d7e85db399

Access to power ups:

We allow Xanadu Quantum Technologies to share our email addresses with the Power-Up Sponsors for the purpose of facilitating the delivery of the Power-Ups.
Yes.
(If applying for AWS’s credits) We have an AWS account
Yes.