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.

About Program

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, hands-on workshops, and live demonstrations, 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.

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.
Practical Simulation Experience: Gain hands-on experience with quantum simulation software to design and test quantum circuits.
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

Certificate

Program Assessment

Module Quizzes: Regular quizzes to test knowledge of quantum mechanics, machine learning fundamentals, and their integration.
Hands-On Projects: Practical assignments requiring the implementation of quantum algorithms using simulation software.
Research Papers: Participants are required to write and submit research papers on contemporary issues in quantum machine learning, which may qualify for special recognition.
Peer-Reviewed Assignments: Collaborative projects where participants critique and improve each other’s work.
Final Examination: A comprehensive test covering all course materials to assess participants’ understanding and capability to apply quantum machine learning concepts.

Program Deliverables

Comprehensive video tutorials and meticulously curated reading materials.
Live demonstrations of quantum computing platforms.
Hands-on workshops using quantum simulation software.
Peer-reviewed assignments and stimulating project work fostering practical application of acquired knowledge.
Weekly interactive sessions with quantum computing experts.
Access to a community forum for continual support, collaborative learning, and networking opportunities.
Certificate of completion to endorse the professional skill set.

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 of this program can pursue various career opportunities, including:

- - Leadership roles in cybersecurity research and development.
  - Specialized positions in organizations focusing on AI-driven security solutions.
  - Consulting roles in cybersecurity strategy and implementation.
  - Academic positions in university cybersecurity programs.

Companies those are actively recruiting

Consortium e-Learning Network Pvt. Ltd.

Quantum Machine Learning: Harnessing Quantum Computing for AI

Batches

Program Aim