Analyzing Machine Learning Tools and Techniques Linked to Quantum Computing

Vanshika Goel

Vol. 8, Jan-Dec 2022

Page Number: 127 - 138

Abstract:

Over the past few decades, the computing industry has experienced significant transformations. Conventional computers carry out user-specified tasks using binary (1s and 0s) integers. A novel approach known as "quantum computing" makes use of the ideas of quantum physics to solve issues that are too complex for traditional computing equipment. Machine learning and quantum computing are two of the scientific domains that are expanding at the quickest rates at now. Recently, studies have been conducted to see whether quantum computing may enhance traditional machine learning techniques. Quantum machine learning refers to hybrid approaches that integrate classical and quantum algorithms, evaluating quantum states instead of regular data using quantum methodologies. Quantum algorithms hold great promise for improving data science methods. In this work, we first provide an overview of the various contributions made by researchers in the field of quantum learning, and we then examine some of the techniques associated with its practical applications.

References

• Endo, Suguru & Cai, Zhenyu & Benjamin, Simon & Yuan, Xiao. (2021). Hybrid Quantum-Classical Algorithms and Quantum Error Mitigation. Journal of the Physical Society of Japan. 90. 032001. 10.7566/JPSJ.90.032001.
• R. Mittal, V. Malik, S. B. Goyal, S. A. Yadav, A. P. Srivastava and A. Sankhyan, 'Spinal Cord Disease Identification Using Transfer Learning Techniques,' https://doi.org/10.1007/978-981-99-0109-8_9.
• Carrasquilla, Juan. (2020). Machine learning for quantum matter. Advances in Physics: X. 5. 1797528. 10.1080/23746149.2020.1797528.
• Costa, N.F., Omar, Y., Sultanov, A. et al. Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors. EPJ Quantum Technol. 8, 16 (2021). https://doi.org/10.1140/epjqt/s40507-021-00105-y.
• Zhang, Yao & Ni, Qiang. (2020). Recent Advances in Quantum Machine Learning. Quantum Engineering. 2. 10.1002/que2.34.
• Bouland, Adam & Giurgica-Tiron, Tudor. (2021). Efficient Universal Quantum Compilation: An Inverse-free Solovay-Kitaev Algorithm.
• Nivelkar, Mukta & Bhirud, S.. (2021). Supervised Machine Learning Strategies for Investigation of Weird Pattern Formulation from Large Volume Data Using Quantum Computing. 10.1007/978- 981-16-2164-2_45.

Back Download