Quantum Machine Learning(QML)

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Why Quantum Machine Learning is Important
in Data Science and AI

Quantum Machine Learning (QML) combines quantum computing with AI to solve complex problems faster than classical methods.

It has the potential to exponentially accelerate computations for high-dimensional optimization and simulation tasks.

  • Quantum systems offer new ways to represent and manipulate data using quantum states and entanglement.
  • QML algorithms can improve performance in tasks like clustering, classification, and regression, especially for large or complex datasets.
  • Quantum-enhanced models can explore larger hypothesis spaces, leading to better generalization and expressivity.
  • In fields like finance, chemistry, and genomics, QML may soon outperform traditional AI models due to faster simulation and modeling.
  • QML encourages a paradigm shift in algorithm design, changing how we think about computing and learning from data.
  • Hybrid quantum-classical models bridge today’s limitations of quantum hardware with classical ML strengths.
  • It opens up a future of AI systems that scale beyond current compute limits, empowering scientific discovery and real-time reasoning.
  • Understanding QML now positions learners at the forefront of next-generation AI and computational research.
qml
01

Module 1: Introduction to Quantum Computing

  1. Classical vs Quantum computing

  2. Qubits, Superposition, Entanglement

  3. Quantum gates and circuits

  4. Measurement and quantum state collapse

  5. Bloch sphere representation

  6. Quantum parallelism

02

Module 2: Introduction to Machine Learning

  1. Brief overview of classical ML

  2. Supervised vs unsupervised learning

  3. Regression, classification, clustering

  4. ML pipeline and computational complexity

  5. Limitations of classical ML with big data

03

Module 3: Foundations of Quantum Information

  1. Quantum bits (qubits) vs classical bits

  2. Multi-qubit systems and tensor products

  3. Quantum operators and unitaries

  4. Quantum state representation using Dirac notation

  5. Circuit model of quantum computation

04

Module 4: Quantum Programming Basics

  1. Setting up Qiskit / PennyLane / Cirq / Braket

  2. Creating quantum circuits

  3. Applying gates and measuring qubits

  4. Simulators vs real quantum hardware (IBM Q, IonQ)

  5. Introduction to hybrid quantum-classical workflow

05

Module 5: Variational Quantum Circuits (VQCs)

  1. Parameterized quantum circuits

  2. Quantum circuit ansatz design

  3. Cost functions and classical optimization

  4. Variational Quantum Classifier (VQC)

  5. Barren plateaus problem and solutions

06

Module 6: Quantum Machine Learning Algorithms

  1. Quantum-enhanced supervised learning

    1. Quantum classifiers

    2. Quantum kernel methods

  2. Quantum k-nearest neighbors (QkNN)

  3. Quantum support vector machines (QSVM)

  4. Quantum principal component analysis (qPCA)

07

Module 7: Hybrid Quantum-Classical Models

  1. Variational Quantum Eigensolver (VQE)

  2. Quantum Approximate Optimization Algorithm (QAOA)

  3. Integration with PyTorch and TensorFlow (PennyLane)

  4. Hybrid architectures for training ML models

08

Module 8: Quantum Data Encoding and Feature Maps

  • Encoding classical data into quantum states:

    • Angle encoding

    • Amplitude encoding

    • Basis and IQP encoding

  • Feature maps and kernel tricks

  • Trade-offs: expressivity vs efficiency

09

Module 9: Quantum Neural Networks (QNNs)

  1. Structure of QNNs

  2. QNN vs classical NN

  3. Training quantum models using gradient descent

  4. Quantum backpropagation and parameter shift rule

  5. Noise-aware QNN training

10

Module 10: Unsupervised and Generative QML

  1. Quantum k-means clustering

  2. Quantum Boltzmann machines

  3. Quantum GANs (Generative Adversarial Networks)

  4. Use cases: molecule generation, image synthesis

11

Module 11: Tools, Libraries, and Platforms

  1. Qiskit (IBM)

  2. PennyLane (Xanadu)

  3. Cirq (Google)

  4. Amazon Braket

  5. D-Wave and quantum annealing SDKs

12

Module 12: Applications of QML

  1. Drug discovery and quantum chemistry

  2. Portfolio optimization in finance

  3. Quantum NLP and document classification

  4. Quantum recommendation systems

  5. QML in physics simulations

13

Module 13: Limitations, Challenges & Future Directions

  1. Noisy Intermediate-Scale Quantum (NISQ) era limitations

  2. Error mitigation and quantum noise

  3. Scalability of QML algorithms

  4. Open problems in QML research

  5. Roadmap to fault-tolerant QML

14

Module 14: Hands-On Projects and Case Studies

  1. Build a quantum classifier on Iris dataset using Qiskit

  2. Quantum kernel SVM for image classification

  3. QAOA for solving Max-Cut problems

  4. Quantum circuit for digit recognition (MNIST)

  5. Comparative study: classical vs quantum ML accuracy

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