Quantum Computing in Big Data Analytics: A Survey

• Published in: 2016 IEEE International Conference on Computer and Information Technology (CIT) pp. 112 - 115
• Main Authors: Shaikh, Tawseef Ayoub, Ali, Rashid
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Big data; Big Data Analytics; Computers; Logic gates; Machine Learning; Machine learning algorithms; Quantum Artificial Intelligence; Quantum Clustering; Quantum computing; Quantum mechanics; Qubits; Support vector machines
• DOI: 10.1109/CIT.2016.79

Big Data is a term which denotes data that is beyond storage capacity and processing capabilities of classical computer and getting some insight from large amount of data is a very big challenge at hand. Quantum Computing comes to rescue by offering a lot of promises in information processing systems, particularly in Big Data Analytics. In this paper, we have reviewed the available literature on Big Data Analytics using Quantum Computing for Machine Learning and its current state of the art. We categorized the Quantum Machine learning in different subfields depending upon the logic of their learning followed by a review in each technique. Quantum Walks used to construct Quantum Artificial Neural Networks, which exponentially speed-up the quantum machine learning algorithm is discussed. Quantum Supervised and Unsupervised machine learning and its benefits are compared with that of Classical counterpart. The limitations of some of the existing Machine learning techniques and tools are enunciated, and the significance of Quantum computing in Big Data Analytics is incorporated. Being in its infancy as a totally new field, Quantum computing comes up with a lot of open challenges as well. The challenges, promises, future directions and techniques of the Quantum Computing in Machine Learning are also highlighted.