用量子神经网络高效分类多比特量子态，避免梯度消失问题。
Quantum neural networks facilitating quantum state classification
- 设计可调纠缠能力的双量子比特门电路，结合自定义参数化方案构建网络。
- 在二分类与多分类任务中均实现高效识别，有效缓解梯度消失现象。
- 适用于多比特纯量子态分类，为后续通用化提供基础。
量子态分类面临重大挑战。本文结合问题导向电路与定制/预定义参数化方案，构建量子神经网络解决该问题。通过引入具有可调纠缠能力的双量子比特参数化酉门，构建量子态数据集并集成至网络结构中。可视化结果表明，所选参数化方案能有效缓解梯度消失（barren plateaus）问题。实验验证了该网络在二分类与多分类任务中的高效性。本工作为多比特量子态分类建立基础，并具备推广至多比特纯量子态的潜力。
原文摘要 · Abstract (English)
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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