Online/ e-LMS
Self Paced
Advanced
8 Weeks
About
It is an 8-week intensive course designed for advanced undergraduates, graduate students, and professionals interested in the intersection of quantum computing and AI. Through comprehensive tutorials, participants will learn to simulate quantum circuits, develop quantum algorithms, and apply these in AI tasks. The course offers a unique opportunity to explore this cutting-edge technology, preparing participants to contribute to the future of quantum machine learning.
Aim
The course aims to provide participants with a comprehensive understanding of quantum computing fundamentals and their applications in machine learning. This course strives to bridge the gap between quantum technologies and artificial intelligence, empowering participants to develop and implement quantum machine learning algorithms that can outperform classical solutions in speed and efficiency.
Program Objectives
- Understand Quantum Computing and AI: Build a solid foundation in the principles of quantum mechanics and its applications to solve complex computational problems.
- Develop Quantum Algorithms: Learn and develop quantum machine learning algorithms, such as quantum neural networks and quantum support vector machines.
- Project Implementation: Plan and execute a quantum machine learning project from inception to testing and optimization on quantum computing platforms.
- Industry Readiness: Prepare for emerging roles in the quantum technology sector by understanding current technologies, potential applications, and industry needs.
Program Structure
Module 1: Introduction to Quantum Computing and Machine Learning
Section 1: Foundations of Quantum Computing
- Principles of Quantum Mechanics
- Quantum Bits and Quantum Gates
- Quantum Circuits
Section 2: Basics of Machine Learning
- Overview of Machine Learning
- Algorithms and Models in Classical Machine Learning
- Data Handling and Preprocessing
Section 3: Integration of Quantum Computing with Machine Learning
- Potential of Quantum Computing in AI
- Case Studies: Quantum Speedups in Algorithmic Tasks
Module 2: Quantum Algorithms for Machine Learning
Section 1: Quantum Algorithms Basics
- Grover’s Algorithm
- Shor’s Algorithm
- Quantum Fourier Transform
Section 2: Quantum Machine Learning Algorithms
- Quantum Principal Component Analysis
- Quantum Support Vector Machines
- Quantum Neural Networks
Module 3: Implementing Quantum ML Models
Section 1: Quantum Programming Languages
- Qiskit
- Microsoft Q#
- Forest by Rigetti
Section 2: Simulation and Real Quantum Computers
- Using IBM Quantum Experience
- Cloud-based Quantum Computing
Section 3: Project Development
- Project Planning and Design
- Implementation of a Quantum ML Model
- Testing and Optimization on Quantum Devices
Participant’s Eligibility
This course is suitable for advanced undergraduates and graduate students in Computer Science, IT, and Electronics, as well as professionals in IT services, Consulting, and Analytics services looking to explore new technologies. Basic understanding of quantum computing and machine learning concepts is recommended.
Program Outcomes
Upon completion of this course, participants will:
- Gain a robust understanding of Quantum Machine Learning and its applications in AI.
- Develop practical skills in implementing machine learning algorithms on quantum devices.
- Acquire hands-on experience through simulations of quantum circuits and real-world projects.
- Explore the integration of quantum computing with machine learning to solve complex problems.
- Be prepared to contribute to the rapidly growing field of Quantum Machine Learning in academia or industry.
Fee Structure
Standard Fee: INR 10,998 USD 198
Discounted Fee: INR 5499 USD 99
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Placement Assistance
- Networking Opportunities: Organized events with industry leaders from IT and analytics to help participants connect with potential employers.
- Career Development Workshops: Sessions on resume building, interview preparation, and job application strategies tailored to the tech industry.
- Corporate Guest Lectures: Insights from professionals in quantum computing and AI, offering participants an inside look at industry demands and opportunities.
Future Career Prospects
Graduates can pursue careers in:
- Quantum Computing Research
- AI-driven Quantum Applications
- Data Science with Quantum Technologies
- Quantum Software Development
- Academic and Industrial Research in Quantum AI
Companies those are actively recruiting
| Company Name | Location | Industry Focus | Hiring Status |
|---|---|---|---|
| Amazon | |||
| IBM | |||
| Microsoft |
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