Li Yaochong, Zhang Jing, Zhou Rigui, Qu Yi, Xu Ruiqing
College of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.
Research Center of Intelligent Information Processing and Quantum Intelligent Computing, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.
Entropy (Basel). 2025 Jul 8;27(7):733. doi: 10.3390/e27070733.
Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QNN, the parameterized quantum circuit (PQC) plays a crucial role in determining the QNN's overall performance. However, quantum circuit architectures designed manually based on experience or using specific hardware structures can suffer from inefficiency due to the introduction of redundant quantum gates, which amplifies the impact of noise on system performance. Recent studies have suggested that the advantages of quantum evolutionary algorithms (QEAs) in terms of precision and convergence speed can provide an effective solution to quantum circuit architecture-related problems. Currently, most QEAs adopt a fixed rotation mode in the evolution process, and a lack of an adaptive updating mode can cause the QEAs to fall into a local optimum and make it difficult for them to converge. To address these problems, this study proposes an adaptive quantum evolution algorithm (AQEA). First, an adaptive mechanism is introduced to the evolution process, and the strategy of combining two dynamic rotation angles is adopted. Second, to prevent the fluctuations of the population's offspring, the elite retention of the parents is used to ensure the inheritance of good genes. Finally, when the population falls into a local optimum, a quantum catastrophe mechanism is employed to break the current population state. The experimental results show that compared with the QNN structure based on manual design and QEA search, the proposed AQEA can reduce the number of network parameters by up to 20% and increase the accuracy by 7.21%. Moreover, in noisy environments, the AQEA-optimized circuit outperforms traditional circuits in maintaining high fidelity, and its excellent noise resistance provides strong support for the reliability of quantum computing.
量子神经网络（QNNs）是一种新兴技术，它使用量子计算机进行神经网络计算。在某些任务中，QNNs已展现出优于经典神经网络的潜在优势。作为QNN的核心组件，参数化量子电路（PQC）在决定QNN的整体性能方面起着至关重要的作用。然而，基于经验手动设计或使用特定硬件结构设计的量子电路架构，可能会由于引入冗余量子门而效率低下，这放大了噪声对系统性能的影响。最近的研究表明，量子进化算法（QEAs）在精度和收敛速度方面的优势可以为与量子电路架构相关的问题提供有效解决方案。目前，大多数QEAs在进化过程中采用固定的旋转模式，缺乏自适应更新模式会导致QEAs陷入局部最优，难以收敛。为了解决这些问题，本研究提出了一种自适应量子进化算法（AQEA）。首先，在进化过程中引入自适应机制，并采用结合两个动态旋转角度的策略。其次，为了防止种群后代的波动，采用亲本的精英保留来确保优良基因的遗传。最后，当种群陷入局部最优时，采用量子突变机制来打破当前的种群状态。实验结果表明，与基于人工设计和QEA搜索的QNN结构相比，所提出的AQEA可以将网络参数数量减少多达20%，并将准确率提高7.21%。此外，在有噪声的环境中，AQEA优化的电路在保持高保真度方面优于传统电路，其出色的抗噪声能力为量子计算的可靠性提供了有力支持。
