Artificial intelligence based on machine learning and quantum computing can be considered as two hot research areas in the technical field. The dream team they formed together is known as quantum machine learning by scientists. The website of the British journal Nature recently reported that scientists are exploring the potential of this future computing alliance and attempting to understand to what extent it will change or reshape the face of science. Leading technology companies from all over the world, including veteran tech giants such as Google and IBM, as well as startups such as Rigetti in California and IonQ in Maryland, are all studying the potential of quantum machine learning. Scientists engaged in academic research are also interested in this. Scientists from the European Center for Nuclear Research (CERN) are pioneers in the field of quantum machine learning. They have used machine learning to search for clues about certain subatomic particles in the data generated by the Large Hadron Collider. Physicist Sofia Valekosa, head of CERN's Quantum Computing and Machine Learning research group, stated that they hope to use quantum computers to accelerate or improve classical machine learning models. Scientists are trying to answer a big question: Is quantum machine learning more advantageous than classical machine learning in certain situations? Theory suggests that quantum computers can improve computational speed for tasks such as simulating molecules or searching for prime numbers of large integers. But researchers still lack sufficient evidence to prove that machine learning can do the same. However, some scientists have pointed out that even if computational speed cannot be improved, quantum machine learning can still discover certain patterns that classical computers miss. Some researchers also focus on applying quantum machine learning algorithms to certain quantum phenomena. MIT physicist Alam Hartan stated that among all proposed applications of quantum machine learning, this is an area with significant quantum advantages. Quantum algorithms are not omnipotent. In the past 20 years, quantum computing researchers have developed a large number of quantum algorithms that theoretically can improve the efficiency of machine learning. In 2008, Ha Tan and others collaborated to invent a quantum algorithm that is several times faster than classical computers in solving large linear equations. But in some cases, quantum algorithms may not perform as well. In 2018, 18-year-old computer scientist Tang Yiwen invented a new recommendation algorithm that can run and complete calculations on traditional computers. Compared with previous recommendation algorithms, this algorithm achieves exponential acceleration and defeats a quantum machine learning algorithm designed in 2016. Tang Yiwen expressed that she holds a "very skeptical" attitude towards any claims that quantum algorithms can accelerate machine learning. However, computational speed is not the only criterion for evaluating the quality of quantum algorithms. There are indications that quantum artificial intelligence systems driven by machine learning can learn to recognize patterns in data, while classical artificial intelligence systems may miss these patterns. Carl Jensen from the Particle Physics Laboratory at the German Institute for Electron Synchrotron Accelerator (DESY) explains that this may be due to quantum entanglement between qubits, which creates correlations between data that classical algorithms find difficult to detect