Quantum Machine Learning: Opportunities and Challenges

Project: Others

Project Details

Abstract

Quantum machine learning (QML) is an emerging discipline that integrates quantum computing and machine learning principles. It can process and analyze large datasets more efficiently than classical algorithms, significantly improving performance and efficiency. QML models offer distinct advantages over classical ML models in addressing challenges due to their inherent quantum characteristics such as parallelism, entanglement and superposition. Quantum computers have the potential to exponentially speed up certain machine learning tasks, such as database search, optimization, and pattern recognition, through algorithms like Grover's and quantum support vector machines. Furthermore, it enables the exploration of high-dimensional feature spaces more efficiently than classical methods, capturing complex relationships and dependencies and potentially leading to the discovery of novel patterns and insights in data. However, realizing this potential requires overcoming significant technical challenges while capitalizing on the unique capabilities offered by quantum computing, such as limited qubits, coherence times, data encoding and scaling up quantum hardware to support complex QML algorithms. Therefore, this two-day workshop will discuss the potential challenges and opportunities of QML and showcase successful applications of QML in various domains.

Submitting Institute Name

Hamad Bin Khalifa University (HBKU)
Sponsor's Award NumberCWSP23-W-0330-24047
Proposal IDEX-QNRF-CWSP-19
StatusNot started
Effective start/end date4/02/255/02/25

Primary Theme

  • Artificial Intelligence

Primary Subtheme

  • AI - Analytics & Decision Support

Secondary Theme

  • Artificial Intelligence

Secondary Subtheme

  • AI - Education

Keywords

  • Quantum
  • Machine Learning
  • Artificial Intelligence

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