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Lecture notes on data engineering and communications technologies

Review on Quantum Computing Tools and Algorithms

Midhun Madhu, Sunanda Dixit

Published Jan 1, 2020• 1 citation
Publisher
DOI
10.1007/978-3-030-37051-0_80

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Quantum Computing (QC) has evolved into a computational model that can potentially solve problems of classes that cannot be solved by classical systems. This review gives a discussion on the innovation of software frameworks, experimental platforms and quantum computing tools used to compute quantum algorithm development, simulation and implementation. It concentrates on widely used programming environments, which compile abstract quantum models into practical workflows, including Cirq, TensorFlow Quantum, ProjectQ and Qiskit. The paper, besides describing the long-lasting problems related to scalability, error correction, and hardware reliability, also considers the architecture of the modern quantum computing platforms. Finally, new advances are outlined, such as quantum cloud service and the integration of artificial intelligence and quantum computing, and their role in the development of QC to viable and lucrative deployments. All in all, the discussion suggests that even now quantum computing still needs to be taken to the next level in terms of hardware infrastructure and software development that would allow progressing to the next stage of quantum technology testing and implementation.
The main objective of the research is to analyze the incorporation of quantum computing in artificial intelligence, focusing on applications, tools, and challenges. Through a systematic approach of literature review in academic databases, a growing interest in this integration was detected, particularly in fields such as physics, medicine, chemistry, and cybersecurity. It highlights the effectiveness of quantum algorithms in complex Artificial Intelligence issues such as factorization and unordered search. Challenges include the vulnerability of qubits and the demand for scalable quantum hardware. The research recognizes booming tools for quantum data modeling for Artificial Intelligence, examines innovative applications in materials design and agriculture, and suggests areas for future research. It highlights the importance of creating more efficient quantum algorithms and optimizing their integration.
A quantum processor, like any computing device, requires the development of both hardware and the necessary set of software solutions, starting with quantum algorithms and ending with means of accessing quantum devices. As part of the roadmap for the development of the high-tech field of quantum computing in the period from 2020 to 2024, a set of software solutions for quantum computing devices was developed. This software package includes a set of quantum algorithms for solving prototypes of applied tasks, monitoring and benchmarking tools for quantum processors, error suppression and correction methods, tools for compiling and optimizing quantum circuits, as well as interfaces for remote cloud access. This review presents the key results achieved, among which it is necessary to mention the execution of quantum algorithms using a cloud-based quantum computing platform.