Learn Quantum & AI.
Get Certified from IISc & QpiAI.

QpiAI™ Quantum & AI Certification is an easy way to learn and immerse yourself into the world of Quantum Computing and Artificial Intelligence. Everything you need to learn Quantum and AI in one learning package!

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Joint learning program by

Courses from Experts

Courses are taught by Prof. Shalabh Bhatnagar(IISc), Prof. Ujwal Sen(HRI), QpiAI team and experts from around the world! The course content will be updated constantly. Download the complete information brochure here.

Upto 1 year free license for QpiAI Explorer*

QpiAI Explorer is the fastest and easiest way to learn and apply Quantum and AI Concepts. Learn more about QpiAI Explorer.

* – On purchase of certification course.

Certification from IISc and QpiAI

Get ahead of your peers with a recognized certification from IISc and QpiAI.

Learn

Artificial Intelligence

Artificial Intelligence
Machine Learning
Deep Learning

Artificial Intelligence

Machine Learning

Deep Learning

Quantum Computing

Essential Mathematics
Quantum Computation
Quantum Information
Quantum Communication
Quantum Programming
Quantum Applications

Essential Mathematics

Quantum Computation

Quantum Information

Quantum Communication

Quantum Programming

Quantum Applications

Available Courses (3)

Batch 2 | Registration Cut Off Date: 15 June, 2021

AI Certification – Beginner (Level 1)
 

For the ones who want to start learning learning Artificial Intelligence

Price

INR 20,000
Deep discount available for students*

Concepts Covered
 

Artificial Intelligence
Machine Learning
Deep Learning

Duration

3 Months

Starts On

July 1, 2021

6 Months free access
Having Questions? Read the FAQ.

AI Certification – Advanced(Level 2)

For the ones who want a comprehensive course on Artificial Intelligence

Price

INR 30,000 
Deep discount available for students*

Concepts Covered in Level 1 +

Practical and Advanced Machine Learning
Advanced Topics in Deep Learning

Duration

6 Months

Starts On

July 1, 2021

1 Year free access
Having Questions? Read the FAQ.

Joint Certification in AI and Quantum Computing

For the ones who want to dive deep into AI and Quantum

Price  BEST VALUE

INR 50,000
Deep discount available for students*

Concepts Covered in AI Certification (Level 2) +

Essential Mathematics
Quantum Computation
Quantum Information
Quantum Communication
Quantum Programming
Quantum Applications

Duration

6 Months

Starts On

July 1, 2021

1 Year free access
Having Questions? Read the FAQ.

*Offer applicable only for students. T&C Apply.

> See Course Curriculum

AI Certification – Beginner (Level 1)

Chapter 1: Prerequisites for Artificial Intelligence
1.1 Linear Algebra
1.2 Probability Theory
1.3 Bayes Theorem and Statistics
1.4 Calculus and Optimization
Chapter 2: Supervised Machine Learning
2.1 Regression Models
2.2 Linear and Multiple Linear Regression
2.3 Regression Performance Metrics
2.4 Classification Models
2.5 KNN, Logistic Regression
2.6 Support Vector Machines
2.7 Confusion Matrix, ROC Curves
2.8 Decision Tree, Bagging, Boosting and Ensembling
Chapter 3: Unsupervised Machine Learning
3.1 Dimensionality Reduction: PCA
3.2 Clustering: K-Means Clustering
Chapter 4: Deep Learning
4.1 Activation Functions, Feedforward Network
4.2 Back Propagation, Loss Functions, Hyperparameters
4.3 Convolutional Neural Networks
4.4 CNN Architectures for Image Classification
4.5 Recurrent Neural Networks
4.6 Long Short-Term Memory Models
4.7 Autoencoders
Chapter 5: Reinforcement Learning
5.1 Finite Markov Decision Processes
5.2 Value and Policy Iteration (Dynamic Programming)
5.3 Monte-Carlo Methods
5.4 On-policy and Off-Policy Algorithms

AI Certification – Advanced (Level 2)

Chapter 1: Prerequisites for Artificial Intelligence
1.1 Linear Algebra
1.2 Probability Theory
1.3 Bayes Theorem and Statistics
1.4 Calculus and Optimization
Chapter 2: Supervised Machine Learning
2.1 Regression Models
2.2 Linear and Multiple Linear Regression
2.3 Regression Performance Metrics
2.4 Classification Models
2.5 KNN, Logistic Regression
2.6 Support Vector Machines
2.7 Confusion Matrix, ROC Curves
2.8 Decision Tree, Bagging, Boosting and Ensembling
Chapter 3: Unsupervised Machine Learning
3.1 Dimensionality Reduction: PCA
3.2 Clustering: K-Means Clustering
3.3 t-SNE
3.4 Kernel PCA
3.5 Spectral Clustering
Chapter 4: Deep Learning
4.1 Activation Functions, Feedforward Network
4.2 Back Propagation, Loss Functions, Hyperparameters
4.3 Convolutional Neural Networks
4.4 CNN Architectures for Image Classification
4.5 Recurrent Neural Networks
4.6 Long Short-Term Memory Models
4.7 Autoencoders
Chapter 5: Practical Machine Learning
5.1 Exploratory Data Analysis
5.2 Feature Engineering
5.3 Hyperparameter Tuning
5.4 Model Selection
5.5 End-to-end Machine Learning
Chapter 6: Bayesian Methods in Machine Learning
6.1 Bayesian Inference
6.2 Bayesian Optimization
6.3 Variational Methods
6.4 Gaussian Process Regression
Chapter 7: Advanced Topics in Deep Learning
7.1 Object Detection
7.2 Semantic Segmentation
7.3 Generative Adversarial Networks
7.4 Variational Autoencoders
Chapter 8: Reinforcement Learning and Deep Reinforcement Learning
8.1 Finite Markov Decision Processes
8.2 Value and Policy Iteration (Dynamic Programming)
8.3 Monte-Carlo Methods
8.4 On-policy and Off-Policy Algorithms
8.5 Deep Reinforcement Learning
8.6 Value-based methods and Q-learning
8.7 Function Approximation, DQN
8.8 Policy Gradient Methods, Actor Critic Methods

Joint Certification Program in Artificial Intelligence & Quantum Computing

Curriculum Covered in AI Certification (Level 2) and the following

Chapter 1: Prerequisites for Quantum Computing
1.1 Essential Linear Algebra
1.2 Essential Computer Science
1.3 Basics of Quantum Mechanics
Chapter 2: Quantum States and Qubits
2.1 Single-qubit states and superposition
2.2 Single-qubit gates and measurements
2.3 Two-qubit states, entanglement, and Bell’s inequality
2.4 Two-qubit gates and observable
2.5 Multi-Qubit states (GHZ and W states)
2.6 Universal gates and quantum circuit model
2.7 Quantum adiabatic computation and the Ising model for NP-hard problems
Chapter 3: Quantum Algorithms
3.1 Quantum Circuits
3.2 Deutsch-Jozsa Algorithm
3.3 Bernstein-Vazirani Algorithm
3.4 Quantum Fourier Transform
3.5 Quantum Factoring: Shor’s Algorithm
3.6 Quantum Database Search: Grover’s Algorithm
3.7 Circuit Simulations on QpiAI Explorer Software
Chapter 4: Quantum Protocols
4.1 Quantum Teleportation
4.2 Superdense Coding
4.3 Simulation of QpiAI Explorer Software
4.4 Quantum Cryptography and Key Distribution
4.5 Quantum Communication and Networks
Chapter 5: NISQ Devices
5.1 Noise Models
5.2 Quantum Error Mitigation
5.3 Quantum Volume and Performance Metrics
5.4 Hybrid Quantum-Classical Computing
Chapter 6: Quantum Algorithms for Applications
6.1 Quantum Inspired Computing
6.2 Variational Quantum Algorithms
6.3 Variational Quantum Eigensolver
6.4 Quantum Approximate Optimization Algorithm
6.5 Quantum Machine Learning: QNNs
6.6 HHL Algorithm for Solving Linear Systems
Chapter 7: Quantum Hardware: Superconducting Qubits
7.1 Introduction to physical qubits
7.2 Circuit Quantum Electrodynamics
7.3 Transmon and Coupled Qubits
7.4 Control and Readout
Chapter 8: Quantum Hardware: Semiconducting Qubits
8.1 Introduction to physical qubits
8.2 Spin Physics and Quantum Dots
8.3 Control and Readout
8.4 Scalability

Course Structure

Study Material

We have curated an extensive in-depth curriculum for teaching Quantum Computing and Artificial Intelligence. We cover the basics of quantum mechanics, then expand on quantum circuits, algorithms, protocols, and their applications to various science and technology problems. In AI, we cover the basics of mathematics, and then cover traditional machine learning. We stress on supervised and unsupervised learning methods, and deep neural networks.

Assignments and Projects

Students will be supplemented with exercises and assignments based on the study material. They will be guided to read and implement research papers and build projects based on state-of-the-art developments in Quantum Technologies and Artificial Intelligence

QpiAI Explorer

Upto 1 Year License

QpiAI Explorer Software will enable the students to build quantum circuits and experiment with the simulation of quantum algorithms and protocols. Our tool provides detailed explanatory visualizations and even capability to simulate hybrid circuits. The software will also enable the students to build machine learning models and experiment on various classes of problems and datasets. It will teach the students to prepare data, run algorithms, train models, and build ML systems.

How you will learn

Theory Sessions
Practical Sessions
Assignments
All within one tool – QpiAI™ Explorer

Theory Sessions

Practical Sessions

Assignments, Projects and Tests

All within one tool – QpiAI™ Explorer

Certificate shown is for representational purposes only. Actual certificate may vary.

Get Certified

Improve your resume with a certification from IISc and QpiAI India. What’s more? Students who perform excellently* get free 1-year license to QpiAI Pro and free 1-year license to the upcoming QpiAI AI/ML Model Marketplace as both buyer and seller.

*Students who secure above 90% in Level 2 AI Certification or Joint Certification on Quantum and AI are eligible. For QpiAI Pro 1-year free license, cloud computation costs shall be borne by the student/user.

Special Discount for Students & Universites

The easiest way to learn Quantum and AI is available with discount for universities and students. Register with your university email ID to avail the discount.

Frequently Asked Questions

1. How can I enroll in the certificate courses?

Here is a step by step video on how to enroll for the certification courses

2. Would there be an entrance test for the certification?
The QpiAI - IISc Certification Program is open to all candidates and there is no explicit selection criteria for the program. However, the number of seats available for the certification is limited.
3. What are prerequisites for two sets of courses (Quantum, AI) in general?
We do not assume any prerequisites in any of the topics. We will start the course from absolute basics and build the grounds for all concepts required in AI and Quantum. However, basic understanding of matrices, vectors, complex numbers and functions will be helpful.
4. What will be the course structure for the 3 courses?
Here is a video explaining the course structure for the 3 courses:

You can also download the complete course information brochure here:

Download

5. What is the eligibility criteria for a certification program?
The course is open to undergraduate students and postgraduate students in science, technology, and engineering, and to working professionals in any domain of the industry. There is no explicit eligibility criteria.
6. What is the mode delivery for the courses? Is it entirely online or requires physical presence?
The mode of the course is completely online in the form of pre-recorded lectures. The lectures will be streamed through the QpiAI Explorer tool.
7. Can I watch a preview video for the course lecture?
Here is one of the course lectures:

8. How would the hands-on component (projects/assignments) be completed/submitted?
The assignments will be in the form of coding notebooks. The evaluations will be automated.
9. How much weekly workload is expected? (in hours)
The weekly workload for
AI-Level 1 Course is 4.5 hours.
AI-Level 2 Course is 4.5 hours.
Joint AI and Quantum Course is 9 hours.
10. How will I get support for query resolution / project guidance during the course?
The QpiAI team will be live on the discussion forum and will support the resolution of queries as soon as they are posted.
11. Would there be guest lectures from the industry?
The course will have a set of guest lectures from esteemed professors and research practitioners. QpiAI itself being an industry organization will drive the course in an application-centric manner. We have curated the course in such a way that the balance between theory and applications is maintained. This will also reflect in the course assignments.
12. How long will the course material be accessible ?
  1. The AI Level-1 certification is a 3-months program.
  2. The AI Level-2 certification is a 6-months program.
  3. The Joint AI-Quantum Computing Certification is a 6-months program.
  4. Students for Level-1 AI Certification will have access to course materials for 6 months.
  5. Students for Level-2 AI and Joint AI+Quantum Certification will have 1 year access.
13. Would it help for an experienced professional like me to switch domains or do a career transition?
The course will cover applications of quantum computing to various domains like optimization, machine learning, finance, logistics, pharmaceuticals, space technology, and others. This would enable the professionals to develop quantum computational and AI solutions for diverse applications within and across their industries, and lead new research and development projects.

Highly motivated candidates might as well transition to the world of quantum computing technologies and take up roles in taking the industry forward.

14. Could top candidates get an opportunity to work at QpiAI?
QpiAI is committed to building a quantum ecosystem in India, and our certification program is the first step towards it. After successful completion of the course, excellent candidates (consolidating 90% + aggregate score) will

  1. Have an opportunity to be considered for AI as well as Quantum computing jobs at QpiAI
  2. Have 1-year QpiAI Pro License and 1 year free membership to QpiAI Marketplace AI and Quantum Solutions Marketplace
  3. The cloud computation cost for QpiAI Pro will be borne by the candidate
15. Still have questions?

You can also write to us at certification@qpiai.tech and our team will get back as soon as possible.

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