Quantum Computing Fundamentals

    • © Elephant Scale

      May 11, 2023

      Overview

      • Quantum Computing, together Artificial Intelligence, are perhaps the two most promising and impactful areas of technology.
      • Unlike AI, Quantum Computing has not come into general using, but its time is coming.
      • Therefore, forward-looking companies are preparing for the Quantum Computing now.

      Benefits

      • After taking the course, participants will be able to

        • Explain the principles of quantum computing and quantum algorithms.
        • Understand quantum algorithms that are applicable to cryptography, chemist, materials science and other areas.
        • Be ready to lead the quantum computing revolution and estimate its feasibility for their applications.

      Duration:

      • Two days

      Audience:

      • computer scientists, physicists, and engineers.

      Prerequisites

      • Background in software engineering
      • Familiarity with a programming language

      Lab environment

      • A working environment will be provided for students.

      Course Outline:

      Introduction

      • Quantum physics facts
      • Significance of the 2022 Nobel Prize
      • Most promising quantum computing applications

      Fundamentals of quantum computing programming

      • Programming the IBM quantum computer: Qiskit library
      • The quantum computing programming model
      • The qubit
      • System of qubits
      • Superposition and entanglement
      • Inner and outer products
      • Measurements
      • Unitary transformations and gates
      • Observables and expectation values
      • Quantum circuits
      • Quantum algorithms
      • Implementations on a real quantum computer
      • The IBM quantum computer
      • Classes of quantum algorithms

      Grover’s Algorithm

      • Problem definition and background
      • Algorithm description
      • Sample implementation

      Shor’s Algorithm for Integer Factorization

      • Problem definition and background
      • Algorithm description
      • Sample implementation

      Conclusion

      • Summary
      • What’s next?