Quantum Driven Machine Learning
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Quantum Driven Machine Learning Shivani Saini 1 & PK Khosla 1 & Manjit Kaur 1 & Gurmohan Singh 1 Received: 1 June 2020 / Accepted: 10 November 2020 / Published online: 1 December 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Quantum computing is proving to be very beneficial for solving complex machine learning problems. Quantum computers are inherently excellent in handling and manipulating vectors and matrix operations. The ever increasing size of data has started creating bottlenecks for classical machine learning systems. Quantum computers are emerging as potential solutions to tackle big data related problems. This paper presents a quantum machine learning model based on quantum support vector machine (QSVM) algorithm to solve a classification problem. The quantum machine learning model is practically implemented on quantum simulators and real-time superconducting quantum processors. The performance of quantum machine learning model is computed in terms of processing speed and accuracy and compared against its classical counterpart. The breast cancer dataset is used for the classification problem. The results are indicative that quantum computers offer quantum speed-up. Keywords Qubit . Quantum computing . Machine learning . Support vector machine . Big data
1 Introduction The size of datasets has grown rapidly owing to boom of internet-of-things (IoT) devices and systems like smartphones, sensor networks for smart-cities, wireless sensor networks, remote sensing, and radio-frequency identification (RFID) readers, and smart home etc. The global data volume has increased from 4.4 zettabytes in 2013 to 44 zettabytes in 2020 [1]. It has been further predicted that by 2025, the global data volume will reach 163 zettabytes [1]. This exponential increase in data volume has given birth to a new term called big data. Big data field basically deals with new methods of extracting meaningful information from complex or
* Gurmohan Singh [email protected]
1
Centre for Development of Advanced Computing (C-DAC), Mohali, Ministry of Electronics & Information Technology (MeitY), Mohali 160071, India
4014
International Journal of Theoretical Physics (2020) 59:4013–4024
voluminous datasets and analyzing them which are very difficult to be processed with conventional methods, softwares, and hardware computing resources [2]. The developments in quantum computing have provided a tool to handle big data related problems in various fields like machine learning, artificial intelligence, molecular modeling, chemistry, optimization etc. Basically, quantum computing is a revolutionary change in terms of way of processing the data. The classical Turing machines are based on two states True and False which are represented by Boolean bits 1 and 0, respectively whereas quantum computers are capable of manipulating 1, 0, and all linear combinations of 1 and 0 states simultaneously. The basic entity of representing state of quantum systems is called quantum bit (Qubit). The principles of superpositio
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