Quantum support vector machine for big data classification
This work addresses the computational bottleneck in big data classification for researchers and practitioners in quantum computing and machine learning, offering a novel quantum approach.
The authors tackled the problem of scaling support vector machines to big data by implementing a quantum version with logarithmic complexity in vector size and training examples, achieving exponential speed-up over classical polynomial-time sampling algorithms.
Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases when classical sampling algorithms require polynomial time, an exponential speed-up is obtained. At the core of this quantum big data algorithm is a non-sparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.