Introduction to quantum physics – -ary Evolution – Quantum Mechanics – Hilbert space – Quantum Time Evolution -Von Neumann Entropy – Measurement – Schrodinger Equation – Heisenbergs uncertainty principle – Randomness – Computation with Qubits -Matrix Representation of Serial and Parallel Operations – Quantum Boolean Circuits -Periodicity – Quantum Fourier Transform – N-ary Transforms – Search and Quantum Oracle – Grovers Amplification – Circuit Representation – Speeding up the Traveling Salesman Problem -The Generate-and -Test Method – Quantum Problem – Solving -Heuristic Search – Quantum Tree Search -Tarratacas Quantum Production System.

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 – Toffoligate – Gate based Quantum Computer – standard gates and their operations.

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 toolkits.

Pre-Requisite(s): Probability
Course Type: Lab

Course Objectives

• To understand the components of computing in a Quantum world
• To gain knowledge on mathematical representation of quantum physics and operations.
• To write computations in the real world (standard) in a Quantum computer and simulator.

Course Outcomes

CO1: Understand the computation with Qubits
CO2: Apply Quantum algorithms -Fourier Transform and Grovers amplification
CO3: Apply Quantum problem solving using tree search
CO4: Understand and explore the models of Quantum Computer and Quantum Simulation tools

CO-PO Mapping

Evaluation Pattern: 70/30

Note: Continuous assessments can include quizzes, tutorials, lab assessments, case study and project reviews. Midterm and End semester exams can be a theory exam or lab integrated exam for two hours

1. Jack D. Hidary, Quantum Computing: An Applied Approach, Firstedition, Springer International Publishing, 2019
2. N. David Mermin, Quantum Computer Science: An Introduction Firstedition, Cambridge University Press, 2007
3. I. Chuang and M.Nielsen, Quantum Computation and Quantum Information, Cambridge University Press, 2012
4. Michael A. Nielsen & Isaac L. Chuang, Quantum Computation and Quantum Information, 10th Anniversary Edition, Cambridge Press.
5. Phillip Kaye, Raymond Laflamme, and Michele Mosca, An Introduction to Quantum Computing, Oxford University Press, 2007.
6. Stephen Barnett, Quantum Information, Oxford University Press, 2009.