Quantum computing is proving to be very beneficial for solving complex machine learning problems. Quantum computers are inherently excellent in handling and manipulating vectors and matrix operations. The ever increasing size of data has started creating bottlenecks for classical machine learning systems. Quantum computers are emerging as potential solutions to tackle big data related problems. This paper presents a quantum machine learning model based on quantum support vector machine (QSVM) algorithm to solve a classification problem. The quantum machine learning model is practically implemented on quantum simulators and real-time superconducting quantum processors. The performance of quantum machine learning model is computed in terms of processing speed and accuracy and compared against its classical counterpart. The breast cancer dataset is used for the classification problem. The results are indicative that quantum computers offer quantum speed-up.

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量子驱动机器学习

事实证明，量子计算对于解决复杂的机器学习问题非常有益。量子计算机在处理和操纵向量和矩阵运算方面天生就非常出色。不断增加的数据规模已经开始给经典机器学习系统带来瓶颈。量子计算机正在成为解决大数据相关问题的潜在解决方案。本文提出了一种基于量子支持向量机（QSVM）算法的量子机器学习模型来解决分类问题。量子机器学习模型实际上是在量子模拟器和实时超导量子处理器上实现的。量子机器学习模型的性能是根据处理速度和准确性计算的，并与经典模型进行比较。乳腺癌数据集用于分类问题。结果表明量子计算机提供了量子加速。