An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples

• URL: http://arxiv.org/abs/2011.14297v1
• Date: Sun, 29 Nov 2020 06:14:41 GMT
• Title: An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples
• Authors: Siddharth Dangwal, Ritvik Sharma, Debanjan Bhowmik
• Abstract summary: We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.

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