Quantum programming: Working with IBM’S qiskit tool
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.1.11Keywords:
Quantum computers, Qubit, Qiskit, Algorithm, Python programming language, Quantum circuitDimensions Badge
Issue
Section
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 timeAbstract
How to Cite
Downloads
Similar Articles
- Annalakshmi D, C. Jayanthi, A secured routing algorithm for cluster-based networks, integrating trust-aware authentication mechanisms for energy-efficient and efficient data delivery , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Kirti Gupta, Parul Goyal, Modified-multi objective firefly optimization algorithm for object oriented applications test suites optimization , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, G.Deepa, Exploring advancements in deep learning for natural language processing tasks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Mohamed Azharudheen A, Vijayalakshmi V, Improvement of data analysis and protection using novel privacy-preserving methods for big data application , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- N. Suresh Kumar, S.N.Md. Assarudeen, Solving neutrosophic multi-objective linear fractional programming problem using central measures , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- K. Mohamed Arif Khan, A.R. Mohamed Shanavas, Energy efficient techniques for iot application on resource aware fog computing paradigm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- S. Ramkumar, K. Aanandha Saravanan, Martin Joel Rathnam, M. Revathy, Integration of AI and agent-based modeling for simulating human-ecological systems , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- M. Menaha, J. Lavanya, Crop yield prediction in diverse environmental conditions using ensemble learning , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Azar Bagheri Masoudzade, Maryam Ebrahim Nezhad, Appraising social class dimensions on learning motivation of Iranian students: Family studies and their status in focus , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Rajeev P. R., K. Aravinthan, A novel approach for metrics-based software defect prediction using genetic algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

