English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent times, Variational Quantum Circuits (VQC) have been widely adoptedto different tasks in machine learning such as Combinatorial Optimization andSupervised Learning. With the growing interest, it is pertinent to study theboundaries of the classical simulation of VQCs to effectively benchmark thealgorithms. Classically simulating VQCs can also provide the quantum algorithmswith a better initialization reducing the amount of quantum resources needed totrain the algorithm. This manuscript proposes an algorithm that compresses thequantum state within a circuit using a tensor ring representation which allowsfor the implementation of VQC based algorithms on a classical simulator at afraction of the usual storage and computational complexity. Using the tensorring approximation of the input quantum state, we propose a method that appliesthe parametrized unitary operations while retaining the low-rank structure ofthe tensor ring corresponding to the transformed quantum state, providing anexponential improvement of storage and computational time in the number ofqubits and layers. This approximation is used to implement the tensor ring VQCfor the task of supervised learning on Iris and MNIST datasets to demonstratethe comparable performance as that of the implementations from classicalsimulator using Matrix Product States.

Classical Simulation of Variational Quantum Classifiers using Tensor Rings