This chapter describes how to install qiskit and demonstrates the two main backend simulator functions. It demonstrates the working principles of qiskit software development kit on the simple example of a quantum coin. The chapter indicates how to represent simple quantum circuits by unitary matrices using qiskit get unitary command. It indicates step by step how to define quantum circuits on an example of four-bit conjunction. The qiskit software development kit is in constant development and there are some changes between different versions. The Deutsch algorithm exploits the superposition of qubits generated by Hadamard gates and is more powerful than any classical algorithm.
This chapter describes how to install qiskit and demonstrates the two main backend simulator functions. It demonstrates the working principles of qiskit software development kit on the simple example of a quantum coin. The chapter indicates how to represent simple quantum circuits by unitary matrices using qiskit get unitary command. It indicates step by step how to define quantum circuits on an example of four-bit conjunction. The qiskit software development kit is in constant development and there are some changes between different versions. The Deutsch algorithm exploits the superposition of qubits generated by Hadamard gates and is more powerful than any classical algorithm.
This chapter represents our first step into quantum information theory (QIT). The key to operating such a transition is to become familiar with the concept of the quantum bit, or qubit , which is a probabilistic superposition of the classical 0 and 1 bits. In the quantum world, the classical 0 and 1 bits become the pure states |0〉 and |1〉, respectively. It is as if a coin can be classically in either heads or tails states, but is now allowed to exist in a superposition of both! Then I show that qubits can be physically transformed by the action of unitary matrices , which are also called operators . I show that such qubit transformations, resulting from any qubit manipulation, can be described by rotations on a 2D surface, which is referred to as the Bloch sphere . The Pauli matrices are shown to generate all such unitary transformations. These transformations are reversible , because they are characterized by unitary matrices ; this property always makes it possible to trace the input information carried by qubits. I will then describe different types of elementary quantum computations performed by elementary quantum gates, forming a veritable “zoo” of unitary operators, called I , X , Y , Z , H , CNOT, CCNOT, CROSSOVER or SWAP, controlled- U , and controlled-controlled- U . These gates can be used to form quantum circuits , involving any number of qubits, and of which several examples and tools for analysis are provided. Finally, the concept of tensor product , as progressively introduced through the above description, is eventually formalized.
One of the greatest technological advancements in the last century lies in digital computer science. The idea of storing information and performing complicated calculations with the help of bits, i.e., 0 and 1. But due to a sudden surge in data, the classical computer system has been becoming weak in data processing. Quantum computers offer promising substantial speedup over classical computers for many applications. Quantum chip fabrication has made remarkable gains in recent years, with the number of qubits and fidelity growing. In general computing, a binary digit is the smallest unit of information or a bit. “In quantum computing, the term Qubit (Quantum Bit) serves the exact function of the term bit.” IBM Research released the IBMQ Experience in 2018, the first quantum computer that anyone can use and make accessible to a huge audience of different countries through cloud access. IBM also introduced the tool QISKIT (Quantum information software kit), which enables teachers, researchers, and developers to write coding and run their coding on quantum machines. It also includes different packages of quantum computing. In this paper, the author has discussed different steps to install qiskit. Mainly this paper focused on the “programming and application side of quantum computing.” Qiskit tools used in the python programming language. The “quantum circuits” are fabricated with the use of quantum gates and favorable algorithms with less execution time
One of the fundamental steps for realizing a quantum computing circuit is the coding aspect of it. Quantum computing simulations and realizing quantum computing circuits need coding and that too as expected a pretty complex method of coding. In this segment, we will understand coding in Python by developing simple circuits and code for understanding the basics of circuit building using basic quantum gates such as CNOT gates, Hadamard gates, Pauli gates, and more. This chapter will provide a slight insight into all the gates the Qiskit framework offers. So, let’s start with a short description followed by an intro to quantum gates present in the Qiskit module that will help us in coding the circuits.
Abstract This laboratory work presents a practical implementation of the Deutsch–Jozsa algorithm using the Qiskit framework, complemented by the application of artificial intelligence tools such as GPT-based systems to assist in circuit design, debugging, and conceptual analysis. The material is based on educational resources developed by the International Quantum Center at CERN (Switzerland) and aims to bridge theoretical quantum algorithms with executable quantum programs. In Laboratory Work No. 6, participants construct the Deutsch–Jozsa algorithm for n=4n = 4n=4 input qubits within an oracle-based computational model. The circuit includes a quantum register qqq representing the oracle input qubits, an output qubit outoutout, and a classical register ccc for measurement results. The algorithm begins by applying Hadamard (H) gates to all oracle qubits to create a uniform superposition over the input space. The output qubit is prepared using Pauli-X (X) and Hadamard (H) gates to establish the appropriate phase kickback configuration. A visual barrier is introduced in the circuit to clearly separate initialization, oracle evaluation, and interference stages. After the oracle operation, a second layer of Hadamard gates is applied to the input qubits, followed by measurement into classical bits. The resulting measurement pattern allows deterministic distinction between constant and balanced Boolean functions with a single oracle query—demonstrating exponential speedup compared to classical deterministic algorithms. The laboratory exercise emphasizes structured circuit design, oracle modeling, phase interference analysis, and measurement interpretation. Additionally, GPT-based AI tools are used to support code generation, algorithm explanation, and optimization, illustrating how modern artificial intelligence systems can enhance quantum programming workflows. Through simulation and execution on cloud-based quantum hardware, participants gain both theoretical insight and practical experience with one of the foundational quantum algorithms and its implementation in contemporary quantum computing ecosystems.
Although the basic ideas of quantum computation are fairly straightforward, the detailed manipulations required to extract a useful result from an entangled superposition of answers can be rather complicated. There are, however, a small number of algorithms which are simple enough to explain using only elementary methods. The best example is Deutsch's algorithm: as this only requires two qubits its properties are amenable to brute-force matrix calculations, and these calculations can be simplified using a variety of short cuts, which also give some insight into how and why the algorithm works. We will also explore the simplest case of Grover's quantum search algorithm, which is once again simple enough to be tacked by brute force. Finally we will look briefly at two methods used to stabilize quantum computers against errors. Deutsch's algorithm The invention of Deutsch's algorithm can be taken as defining the start of modern quantum computation, and it remains a key example, exhibiting many of the key properties of quantum algorithms in a particularly simple form. (Note, however, that the version of Deutsch's algorithm described here is not in fact the original but a later modification which is both more powerful and easier to understand.) Consider a binary function f from one bit to one bit, that is a function which takes in either 0 or 1 as its input and returns either 0 or 1 as its output. There are four such functions which may be conveniently labeled by their outputs as shown in Table 8.1.