Multiple Embeddings for Quantum Machine Learning

With the development of Shor's algorithm and Grover's search algorithm, quantum computing has shown performance that surpasses classical computers in areas such as cryptography and search problems. In 2019, Google announced that their quantum computer, Sycamore, achieved "quantum supremacy," meaning that for certain specific tasks, the performance of quantum computers exceeded that of the most powerful classical computers. This milestone has garnered wider academic attention to the field. With the transition of quantum computers from theoretical concepts to practical systems, an increasing number of researchers have realized the advantages of quantum computers over classical computers in handling complex computational problems. As a result, research in quantum machine learning, which involves performing machine learning on quantum computers, has seen rapid growth in recent years. Despite its theoretical soundness, the practical application of the quantum machine learning models demonstrates sub-optimal performance on certain datasets, for example, linearly separable datasets [ Bowles et al., 2024 ] .