Quantum neural networks facilitating quantum state classification
Abstract
The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ans\"{a}tz. To facilitate the resource-efficient quantum state classification, we construct the dataset of quantum states using the proposed problem-inspired circuit. The problem-inspired circuit incorporates two-qubit parameterised unitary gates of varying entangling power, which is further integrated with the ans\"{a}tz, developing an entire quantum neural network. To demonstrate the capability of the selected ans\"{a}tz, we visualise the mitigated barren plateaus. The designed quantum neural network demonstrates the efficiency in binary and multi-class classification tasks. This work establishes a foundation for the classification of multi-qubit quantum states and offers the potential for generalisation to multi-qubit pure quantum states.
Cite
@article{arxiv.2504.06622,
title = {Quantum neural networks facilitating quantum state classification},
author = {Diksha Sharma and Vivek Balasaheb Sabale and Thirumalai M. and Atul Kumar},
journal= {arXiv preprint arXiv:2504.06622},
year = {2025}
}