An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples
Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Institution: Indian Institute of Technology Delhi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3109736998