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Course Detail

Course Name Quantum Artificial Intelligence
Course Code 24AI747
Program M. Tech. in Artificial Intelligence
Credits 3
Campus Amritapuri ,Coimbatore

Syllabus

Introduction – artificial intelligence – computation – Cantor’s diagonal argument – complexity theory – Decision problems – P and NP – Church–Turing Thesis – Von Neumann architecture – Problem Solving – Rules – Logic-based operators – Frames – Categorial representation – Binary vector representation – Production System – Deduction systems – Reaction systems – Conflict resolution – Human problem-solving – Information and measurement – Reversible Computation – Reversible circuits – Toffoli gate

 

Introduction to quantum physics – Unitary Evolution – Quantum Mechanics – Hilbert space – Quan- tum Time Evolution – Von Neumann Entropy – Measurement – Heisenberg’s uncertainty principle – Randomness – Computation with Qubits – Computation with m Qubit – Matrix Representation of Serial and Parallel Operations – Quantum Boolean Circuits – Periodicity – Quantum Fourier Transform – Unitary Transforms – Search and Quantum Oracle – Grover’s Amplification – Circuit Representation – Speeding up the Traveling Salesman Problem – The Generate-and-Test Method – Quantum Problem-Solving – Heuristic Search – Quantum Tree Search – Tarrataca’s Quantum Production System.

 

A General Model of a Quantum Computer – Cognitive architecture – Representation – Quantum Cognition – Decision making – Unpacking Effects – Quantum Walk on a graph – Quantum annealing – Optimization problems – Quantum Neural Computation – Applications on Quantum annealing Computer – Development libraries – Quantum Computer simulation tool kits.

Objectives and Outcomes

Preamble

This course deals with how to use quantum algorithms in artificial intelligence. The course also covers Quantum physics-based information and probability theory, and their relationships to artificial intelligence by associative memory and Bayesian networks. Students will get an introduction to the principles of quantum computation and its mathematical framework.

 

Course Objectives

  • To understand how the physical nature, as described by quantum physics, can lead to algorithms that imitate human behavior
  • To explore possibilities for the realization of artificial intelligence by means of quantum computation
  • To learn computational algorithms as described by quantum computation

 

Course Outcomes

 

COs

Description

CO1

Understand the computation with Qubits

CO2

Apply Quantum algorithms – Fourier Transform and Grover’s amplification

CO3

Apply Quantum problem solving using tree search

CO4

Understand and explore the models of Quantum Computer and Quantum Simulation

tools

CO5

Explore open-source Quantum computer libraries for applications

 

Prerequisites

  • Machine Learning
  • Programming Languages
  • Probability

CO-PO Mapping

 

COs

Description

PO1

PO2

PO3

PO4

PO5

CO1

Understand the computation with Qubits

2

2

2

3

CO2

Apply Quantum algorithms – Fourier Transform and Grover’s amplification

2

2

2

3

CO3

Apply Quantum problem solving using tree search

3

3

3

2

3

CO4

Understand and explore the models of Quantum Computer and Quantum Simulation

tools

3

3

3

3

3

CO5

Explore open-source Quantum computer libraries for applications

3

3

2

2

3

Evaluation Pattern

Evaluation Pattern – 70:30

 

  • Midterm Exam – 30%
  • Continuous Evaluation – 40%
  • End Semester Exam – 30%

Text Books / References

Text Book / References

  1. Andreas Wichert, Principles of Quantum Artificial Intelligence, First edition, World Scientific Publishing, 2014
  2. Peter Wittek, Quantum Machine Learning, First edition, Academic Press, 2014.

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