Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces -- this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in O(N logN) time, and inferencing is performed on a universal quantum computer in linear time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit initialized to the zero state and outputs the predicted label. We analyze its performance on the Iris data set and show that the quantum discriminator can attain 99% accuracy in simulation.
翻译:量子计算机具有在高维空间相对快速运行的独特能力 -- -- 这是为了让他们在古典计算机上拥有竞争优势。 在这项工作中,我们提议了一个名为量子计算机分流器的新型量子机器学习模型,该模型利用量子计算机在高维空间运行的能力。量子分析器在O(NlogN)时间里使用量子古典混合算法接受培训,并在线性时间里对通用量子计算机进行推论。量子分析器将从一个特定数据中提取的二进制特征作为输入,同时进行零状态的预测,并输出预测的标签。我们在Iris数据集上分析其性能,并显示量子分析器在模拟中可以达到99%的精确度。