How to program a quantum computer using Qiskit

Chapter 1: Introduction to Quantum SDK

• Recap of Quantum Computing Basics

  • Qubits vs. Classical Bits:

    • Quantum computers utilize qubits, which can be:

      • 0

      • 1

      • Any linear combination of both (superposition).

  • Entanglement:

    • Qubits can be entangled, creating strong correlations between their states.

  • Gates in Quantum Computing:

    • Gates are applied to modify the states of qubits.

    • Measurement is used to obtain results from the quantum states.

• Utilizing a Quantum SDK:

  • Qiskit:

    • The most widely used quantum SDK, built on Python.

    • Simple syntax suitable for beginners to programming.

  • Program Overview:

    • Create a program with two qubits:

      • Put one qubit into superposition.

      • Entangle it with another qubit.

      • Measure both qubits using gates.

  • Quantum and Classical Registers:

    • Quantum registers for quantum computations (one per qubit).

    • Classical registers for storing measurement results to bridge quantum and classical worlds.

Chapter 2: Applying Gates on Qubits

• Applying Gates:

  • Hadamard Gate on Qubit 0:

    • Creates superposition for qubit 0 (equally likely to be measured as 0 or 1).

  • Control Not Gate (cx):

    • Two-qubit gate with control and target qubits.

    • Functions as follows:

      • If control qubit is 1, flip the target qubit state.

    • Results in entanglement of the qubits—correlated states.

• Measurements:

  • Utilize the measure all function to obtain output results.

  • Running the program on a quantum computer yields:

    • Results: 50% chance of outputting 00 or 11, never 01 or 10.

      • Superposition affects the first qubit; entanglement causes the second to follow suit.

• High-Level Algorithms in Qiskit:

  • Qiskit provides higher-level algorithms for those preferring abstraction over low-level circuits.

  • Machine Learning Integration:

    • Qiskit includes algorithms such as Quantum Kernel Class.

    • Use quantum kernel for training/testing data, integrating with classical algorithms (e.g., support vector classification from scikit-learn).

    • Accelerates classical applications with quantum techniques.

Chapter 3: Wrap Up

• Viewer Engagement:

  • Encourage questions in comments for further clarification.

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