Quantum neural networks facilitating quantum state classification

2025/04/09 by Diksha Sharma, Vivek Balasaheb Sabale, Sharma, Diksha +4
Computer Science · Physics and Astronomy · #Artificial neural network #Construct (python library) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum gate #Quantum many-body systems #Quantum network #Quantum phase estimation algorithm #Quantum state #Unitary state

paper · pdf · doi:10.48550/arxiv.2504.06622

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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 ansätz. 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 ansätz, developing an entire quantum neural network. To demonstrate the capability of the selected ansätz, 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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