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Quantum Computing: Computational Excellence for Society 5.0
Published in Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh, Data Science and Innovations for Intelligent Systems, 2021
Paul R. Griffin, Michael Boguslavsky, Junye Huang, Robert J. Kauffman, Brian R. Tan
Available software. There is a range of software development kits (SDKs) available for writing and running quantum programs, including Qiskit from IBM, Cirq from Google, QDK from Microsoft, Forest from Rigetti, and ProjectQ from ETH Zurich (LaRose, 2019). All of these SDKs, except QDK, are based on Python, which allows easy integration of the Python ecosystem’s capabilities for scientific computing and ML. Many companies also work on quantum software packages for specific domain applications to interface with the SDKs mentioned previously. Notable examples include: Qiskit Aqua for chemistry, ML and optimization, from IBM; PennyLane for ML from Xanadu; OpenFermion for chemistry, from Google; and TensorFlow Quantum for ML, from Google.
Function Optimization Using IBM Q
Published in Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves, Hybrid Quantum Metaheuristics, 2022
Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves
QISKit [21] is an open-source quantum computing software development kit for leveraging today's quantum processors in research, education, and business. It is a Python-based software library that can be used to create quantum computing programs, compile, and execute them on one of several backends [22]. So, QISKit can be installed on top of Python using the below command pip install qiskit This is also available in IBM Q Experience with the Qiskit Notebooks as shown in Figure 3.7.
Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm
Published in IETE Journal of Research, 2023
Tarun Kumar, Dilip Kumar, Gurmohan Singh
QML utilizes QISKIT [48], an IBM Quantum library, to construct and run quantum circuits/algorithms. While classical SVM operates on a local CPU environment, QSVM requires QISKIT. The quantum instance comprises Qiskit terra backend and configuration for transpiling and executing the quantum circuit. This experiment employs three IBMQ backends, namely statevector_simulator [49], ibmq_qasm_simulator [50], and real-time quantum computer ibmq_lima [51]. The QSVM uses the quantum instance to execute the quantum circuits on these backends. The local machine used to run the SVM-based classification model and to access the quantum backends runs on Windows 11, with an i5 processor, 8 GB RAM, and 512 GB SSD. Table 1 provides the characteristics of the IBM backends used in this experiment.