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  • New evidence that quantum machine learning outperforms . . .
    Quantum machine learning models can achieve ‘quantum advantage’ by solving a complex class of mathematical problems impossible to crack with a classical computer, according to new research by UBC material scientists
  • Quantum machine learning: A comprehensive review of . . .
    The survey outlines open research problems in quantum machine learning, including improving quantum kernel methods, developing noise-tolerant quantum circuits, and scaling quantum models for practical use, as well as speculating on interdisciplinary applications and hybrid quantum-classical architecture innovations
  • Challenges and opportunities in quantum machine learning
    QML is usually considered for four main tasks These include tasks where the data are either classical or quantum, and where the algorithm is either classical or quantum Top left: tensor
  • Quantum machine learning: Classifications, challenges, and . . .
    Quantum machine learning algorithms necessitate the transformation of classical data into quantum states, a task laden with challenges The method chosen for encoding exerts a critical influence on the accuracy and efficiency of the algorithm
  • Quantum Advantage in Machine Learning: A Comparative Study of . . .
    We examine the advantages of quantum algorithms, such as quantum parallelism and entanglement, and their potential to outperform classical algorithms in tasks such as classification, clustering, and optimization
  • Machine Learning: Quantum vs Classical | IEEE Journals . . .
    In this paper, we present an overview of quantum machine learning in the light of classical approaches Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain
  • Quantum Machine Learning: Hybrid System of Quantum and . . .
    To combine quantum and classical machine learning algorithms, it is crucial to comprehend the task’s information, structure, and properties of both classical and quantum algorithms, consider the limitations of near-term devices, and ultimately design an efficient scheme





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