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arXiv · 2504.06622

Quantum neural networks facilitating quantum state classification

Diksha Sharma·Vivek Balasaheb Sabale·Thirumalai M.·Atul Kumar
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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.

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Diksha Sharma, Vivek Balasaheb Sabale, Thirumalai M., Atul Kumar. 2025-04-09. Quantum neural networks facilitating quantum state classification. https://arxiv.org/abs/2504.06622

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