This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.
This paper explains the roadmap for running quantum circuit programs based on the Qiskit library in quantum simulators as well as real cloud-based IBM quantum computers. Qiskit is a free and open-source software development platform based on the Python programming language that is used in quantum programming. Qiskit acts as a link between quantum computing’s theoretical foundations and the practical aspects of programming and experimentation. It also allows users to experiment with and develop quantum algorithms, as well as simulate and execute them on simulators and real-world cloud-based quantum devices. It also simplifies the quantum programming process and allows a diverse range of people to participate in the exciting world of quantum computing. The paper, on the other hand, provides the mathematical foundation for analyzing quantum circuits and algorithms using linear algebra principles, as they provide the tools needed to describe and manipulate quantum states and operations. Furthermore, the paper shows quantum circuit design and implementation using real Qiskit codes.
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
Abstract This practical guide explores the use of IBM Quantum cloud simulators for modeling and analyzing quantum circuits under realistic conditions. Using the IBM Q platform and Qiskit Runtime, the work demonstrates how to configure the ibmq_qasm_simulator and map a basic noise model derived from a physical IBM Quantum™ device. The guide focuses on: Setting up the ibmq_qasm_simulator in Qiskit. Extracting and applying a hardware-based noise model from a real IBM Quantum backend. Performing noisy simulations of QuantumCircuits. Using Sampler and Estimator primitives in Qiskit Runtime to analyze circuit outputs. Studying the impact of realistic quantum errors such as decoherence, gate infidelity, and readout noise. By incorporating noise models into simulations, researchers and developers can better understand how quantum circuits behave on real hardware, optimize algorithms before deployment, and design more robust quantum programs. This approach bridges the gap between idealized simulations and physical quantum devices, making it a critical step toward practical quantum application development.
Nowadays, quantum computers are capturing the attention of researchers because of their potential capability to solve problems otherwise not solvable with classical computers. In parallel to the constant research aimed at solving problems related to the physical implementation of such computers, there is also another branch of research trying to develop quantum algorithms. Many of the existing algorithms today need to perform arithmetic operations. In this thesis, the development of a software library of quantum arithmetic circuits - capable of interfacing with the main languages related to the world of Quantum Computing, such as Qiskit, Cirq and T-ket - is presented. With the aim of implementing this software library, the basics of quantum computation were first studied and subsequently an extensive bibliographic research was carried out, aimed at identifying the best performing arithmetic circuits currently available. After determining the circuits to be implemented, the software development has begun using the Python language. During the development of the library, with the aim of making simpler future development, code's modularity - by organizing the general package in many subpackages - and readability - by respecting the guidelines proposed in the PEP8 style guide - were looked for. In addition to what has already been said, a detailed documentation has been produced using Sphinx. Once the development of the library was completed, the functionality of each circuit was tested with simulations on classical computers; moreover, some components were tested on real free-accessible quantum computers, in order to characterize their behavior. Even though the results obtained on real hardware proved that many of the circuits implemented in this library will be reliably executed by more sophisticated and performing quantum computers, this library can be currently employed in an effective way in the simulation of quantum circuits on classical computers.
The Quantum Cookbook: Recipes for Programming Real Qubits is a hands-on programming book that teaches working engineers how to write and run real quantum circuits on real quantum hardware. Organized as 19 recipes that build on each other — from a first "Hello, Qubit" circuit through entanglement (Bell and GHZ states), quantum teleportation, superdense coding, Grover's search, the Quantum Fourier Transform and Shor's algorithm, VQE, and QAOA — the book treats quantum programming the way a culinary cookbook treats cooking: each recipe lists its ingredients (qubits, gates, classical bits), walks through the method step by step, shows the code in two languages side by side, and ends with tasting notes on what the result means. Every recipe is presented in both Qiskit (IBM's Python SDK) and OpenQASM 3.0 (the standardized assembly-level language), and runs on the IBM Quantum Platform via the SamplerV2 and EstimatorV2 primitives, with 10 minutes of free real-hardware time per month on the Open Plan. Unlike a traditional textbook, the book assumes the reader is an engineer, not a physicist, and grounds every concept in runnable code: qubits as 2-dimensional complex vectors, gates as unitary matrices, and multi-qubit states as tensor products are the four math tools the recipes use and nothing more. A math primer, API reference, OpenQASM 3.0 reference, glossary, and citation appendix support the recipes, while dedicated chapters on error mitigation, logical qubits and the fault-tolerance roadmap, and the state of the field in 2026 keep the focus firmly on what runs on a real QPU today and what will run on one tomorrow. Released under the Apache 2.0 License (DOI: 10.5281/zenodo.21863658), it is written for anyone who wants to move past toy simulators and start programming real qubits.
Recent advancements in computing speed and capacity of Artificial Intelligence (AI) algorithms have reached a saturation level in performance due to the continuous application of Moore's law which resulted in the memory wall phenomenon. Quantum computing (QC), based on the principles of quantum mechanics which include superposition and entanglement, has the potential to contribute to the advancements of AI by providing exponential speedup. Quantum computers use quantum bits, also known as qubits, as the metric to demonstrate the power of quantum technology. Qubits, the fundamental unit of quantum information can exist in two states, 0 and 1, simultaneously, leveraging the principle of superposition. Quantum gates are the building blocks of quantum circuits, operating on qubits to transform one quantum state into another, and are comparative to classical logic gates. Designing and simulating quantum circuits can be completed using the Qiskit library design by IBM. Qiskit is a cloud-based, open access platform to real quantum computers with 5 to 16 qubits. This paper offers a straightforward tutorial on implementing one, two and three-qubit quantum gates, as well as designing quantum circuits using the Qiskit library, illustrated with an example of a quantum half adder implementation.