Indian Institute of Technology Kanpur

Advanced Certificate Programme in Quantum Computing and Machine Learning

Indian Institute of Technology Kanpur

Quantum Computing

Application Deadline Approaching

Last date to apply: November 13, 2026 · Program starts on November 15, 2026

About Programme

Learn about quantum computing and machine learning in research, engineering, and product roles.

Learn to become a quantum-aware ML engineer with quantum information, algorithms, and ML design skills.
Build a capstone project that applies hybrid quantum-classical methods to solve real problems end-to-end.
Work on cloud quantum hardware, NISQ devices, and simulators for the purpose of practical experimentation.
Learn VQE, QAOA, Grover, quantum PCA, and optimization methods used in industry instances .
Utilize case studies and labs on cryptography, finance, logistics, and ML acceleration.
Built for professionals: 8 months, weekend live classes, 100 hours, mentoring, and support.

What you'll learn

Use Quantum Computing and Machine Learning to Solve Complex Business and Technology Issues.
Build Practical Quantum Circuits, Algorithms, and Hybrid Quantum-Classical Workflows.
Utilize Quantum Methods for Optimization, Simulation, and Emerging Real-World Use Cases.
Work with Leading Quantum Platforms and Cloud-Based Systems to Gain Hands-On Experience.
Learn How Quantum Machine Learning Can Support Smarter, Faster Decision-Making.
Evaluate Quantum and AI-Powered Solutions with a Strong Focus on Impact, Practicality, and Responsible Use.
Demonstrate Your Learning Through Applied Projects, Industry-Relevant Exercises, and a Capstone Project.

Skills you'll gain

Quantum Computing FundamentalsQuantum Information TheoryQuantum Circuit Design & CompilationQuantum Algorithms (QFT, Grover, Variational Algorithms)Quantum Machine Learning Model DevelopmentHybrid Quantum–Classical WorkflowsQuantum Programming (Qiskit, Cirq, Pennylane)Cloud Quantum Platforms (IBM Quantum, Amazon Braket)Quantum Simulation TechniquesOptimization for Quantum Systems (QUBO, Ising formulations)Feature Engineering & Representation Learning

Programme Content

Foundations of Quantum Computing

Key Topics:

  • History of Quantum Computing
  • Classical vs Quantum Computing
  • Quantum Advantage
  • Industry Landscape

    • Learning Outcomes:

  • Quantum simulators
  • First quantum programs
  • Quantum workflow demonstration

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    Mathematics for Quantum Computing

    Key Topics:

  • Linear Algebra
  • Complex Numbers
  • Vector Spaces
  • Matrices and Operators
  • Probability Theory

    • Learning Outcomes:

  • Quantum state calculations
  • Matrix operations using Python
  • State visualization

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    Quantum Mechanics for Computing

    Key Topics:

  • Qubits
  • Superposition
  • Entanglement
  • Measurement
  • Bloch Sphere

    • Learning Outcomes:

  • Qubit simulations
  • Bloch sphere visualization
  • Entanglement experiments

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    Quantum Gates & Quantum Circuits

    Key Topics:

  • Single Qubit Gates
  • Multi-Qubit Gates
  • Universal Gates
  • Circuit Design

    • Learning Outcomes:

  • Quantum circuit development
  • Gate implementations
  • Circuit optimization

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    Quantum Programming with Qiskit

    Key Topics:

  • Quantum SDKS
  • Qiskit Architecture
  • Circuit Construction

    • Learning Outcomes:

  • Qiskit coding labs
  • Quantum program execution
  • Real quantum hardware access

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    Quantum Algorithms

    Key Topics:

  • Deutsch-Jozsa Algorithm
  • Bernstein-Vazirani Algorithm
  • Grover Search
  • Quantum Fourier Transform

    • Learning Outcomes:

  • Algorithm implementation
  • Performance analysis
  • Quantum speedup demonstrations

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    Quantum Optimization

    Key Topics:

  • Optimization Fundamentals
  • QAOA
  • Combinatorial Optimization
  • Logistics Applications

    • Learning Outcomes:

  • Optimization problem modelling
  • QAOA implementation
  • Route optimization

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    Quantum Machine Learning Foundations

    Key Topics:

  • QML Landscape
  • Hybrid Computing
  • Variational Circuits
  • Data Encoding

    • Learning Outcomes:

  • Quantum datasets
  • Variational circuit creation
  • Hybrid workflows

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    Quantum Neural Networks

    Key Topics:

  • Quantum Perceptrons
  • Variational Quantum Circuits
  • Quantum Neural Networks

    • Learning Outcomes:

  • QNN implementation
  • Classification models
  • Training workflows

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    Quantum Al & Generative Models

    Key Topics:

  • Quantum-enhanced Al
  • Quantum Generative Models
  • Quantum Reinforcement Learning
  • Future of Quantum Al

    • Learning Outcomes:

  • Hybrid Al workflows
  • Quantum-assisted ML models

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    Quantum Cryptography & Security

    Key Topics:

  • Quantum Key Distribution
  • Post-Quantum Cryptography
  • Quantum Security Landscape

    • Learning Outcomes:

  • QKD simulations
  • Cryptographic demonstrations

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    Quantum Cloud Platforms

    Key Topics:

  • Quantum Hardware
  • NISQ Era
  • Cloud Quantum Computing

    • Learning Outcomes:

  • IBM Quantum
  • Amazon Braket
  • Microsoft Azure Quantum

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    Industry Applications of Quantum Computing

    Key Topics:

  • Finance
  • Healthcare
  • Drug Discovery
  • Supply Chains
  • Aerospace

    • Learning Outcomes:

  • Industry case studies
  • Use-case development workshops

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    Emerging Quantum Technologies

    Key Topics:

  • Fault-Tolerant Quantum Computing
  • Quantum Internet
  • Quantum Sensors
  • Quantum Communication

    • Learning Outcomes:

  • Research trend analysis
  • Future technology assessments

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    Capstone Project

    Learning Outcomes:

    Participants will develop one of the following:

  • Quantum ML Classifier
  • Quantum Portfolio Optimization
  • Quantum Drug Discovery Workflow
  • Quantum Cryptography Solution
  • Quantum Supply Chain Optimizer
  • Quantum Al Application

    • Download Full Curriculum

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    Advanced Certificate Programme in Quantum Computing and Machine Learning | IIT Kanpur