Quantum Driven Machine Learning

Saini S., Khosla P., Kaur M., Singh G.

International Journal of Theoretical Physics, vol.59, no.12, pp.4013-4024, 2020 (SCI-Expanded, Scopus) identifier identifier

  • Nəşrin Növü: Article / Article
  • Cild: 59 Say: 12
  • Nəşr tarixi: 2020
  • Doi nömrəsi: 10.1007/s10773-020-04656-1
  • jurnalın adı: International Journal of Theoretical Physics
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, INSPEC, zbMATH
  • Səhifə sayı: pp.4013-4024
  • Açar sözlər: Big data, Machine learning, Quantum computing, Qubit, Support vector machine
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Yox

Qısa məlumat

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.