Quantum Machine Learning: Harnessing Quantum Computing for AI

Quantum Computing, Machine Learning, Quantum Algorithms

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Online/ e-LMS
Self Paced
8 weeks


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.


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






Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam Weightage
Mid Term Assignments 20 %
Final Online Exam 30 %
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





Amazon, Google, IBM, Microsoft

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