Quantum machine learning
LU Si-cong
ZHENG Yu
WANG Xiao-ting
WU Re-bing
Abstract：Artificial intelligence and quantum physics are two most influential disciplines developed in the last centu-ry. Recent years, their marriage in data science attracted much attention, forming the frontier field of quantum machine learning. Exploiting quantum superposition, quantum machine learning provides the hope for resolving difficulties in big data and training process, and new learning models illuminated by quantum physics. So far, the field is still in its infancy. Although many problems had been explored, a systematic theory is still lacking. This review will summarize the major differences in data structure and algorithms between classical and quantum machine learning, as well as the key accel-eration techniques. Several topics will be covered, including the data structure (digital and analog), computation skills (phase estimation, Grover search and inner product), fundamental algorithms (linear equation, principle component analy-sis and gradient algorithms) and several learning models (supporting vector machine, nearest-neighbor method, perceptron networks and Boltzmann machine). Finally, we propose several promising directions in this field.
Keywords：quantum mechanicsquantum computationmachine learningdeep learningneural network
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1429-1436 )