Quantum neural networks facilitating quantum state classification

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"atz. 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"atz, developing an entire quantum neural network. To demonstrate the capability of the selected ans"atz, 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.