Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm

AbstractQuantum computing is an emerging field that can effectively solve several machine learning problems. The immensely growing data size has begun to create barriers for classical machine learning algorithms. Quantum computers proficiently handle and process big data composed of metrics and vectors. This paper presents the quantum machine learning model based on quantum support vector machines (QSVM) to classify brain tumours into malignant and benign. The dataset used in this experiment is Brats 2015, available on Kaggle. The QSVM-based model was run on real-time quantum machines and simulators to extract the performance metrics in terms of accuracy and execution time. The QSVM classification model is compared with its classical SVM equivalent. The kernel-based QSVM model implementation on a 32-qubit quantum simulator was 188 times faster with 95% accuracy which is 1.60% better than the classical equivalent. Similarly, the model takes 24.19% less time than the classical SVM model when realized on a 5-qubit real-time superconducting processor with the same accuracy. Hence, the results reveal that quantum machine learning models implemented on quantum computers outperformed their classical equivalents.Keywords: Brain tumourClassifiersQuantum machine learningQuantum support vector machinesQuantum feature mappingSVM ACKNOWLEDGEMENTSThe authors are highly grateful to IBM Quantum for providing access to their software and hardware computational resources through the cloud network.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsTarun KumarTarun Kumar received his BTech degree in electronics and communication engineering from Punjab Technical University, Jalandhar, India in 2013 and his ME degree in electronics and communication engineering from Panjab University, Chandigarh, India in 2018. He is currently pursuing a PhD in electronics and communication engineering at the department of electronics and communication, Sant Longowal Institute of Engineering and Technology (Deemed to be University) Longowal, Sangrur, Punjab, India. His areas of interest are quantum computing, quantum search algorithms, and quantum machine learning. Corresponding author. Email: tarunkumar2992@gmail.comDilip KumarDilip Kumar received his BE degree in electronics and telecommunication engineering from the Army Institute of Technology, University of Pune, India, in 2000, his ME degree in electronics product design and technology from the Panjab University, Chandigarh, India, in 2003, and a PhD degree in faculty of engineering and technology from the M.M. University, Mullana, India, in 2010. He is currently working as a professor at the department of electronics and communication engineering, Sant Longowal Institute of Engineering and Technology (Deemed to be University) Longowal, Punjab, India. He appeared in the world’s top 2% scientists list published by Stanford University in 2021. His research interests include quantum computing, quantum machine learning, embedded systems, electronic system design, wireless sensor networks, and vehicular ad hoc networks. He has published more than 60 research papers in reputed international journals and conferences. Email: dilip.k78@gmail.comGurmohan SinghGurmohan Singh received his BTech degree in electronics and communication engineering from Giani Zail Singh College of Engineering and Technology Bathinda, India in 2001, an MTech degree in microelectronics from Panjab University, Chandigarh, India in 2005, and a PhD degree in electronics and communication engineering from NIT Jalandhar, India in 2019. He is currently working as a joint director at the Cyber Security Technology Division (CSTD), Centre for Development of Advanced Computing (C-DAC), Mohali, India. His areas of interest are quantum computing, quantum machine learning, VLSI, computational nanoscience, molecular electronics, and MEMS/NEMS sensors. Email: gurmohan@cdac.in