用进化算法自动设计高容量量子电路，提升量子机器学习性能
Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity
- 将量子电路结构编码后通过进化算法优化
- 基于有效维度的适应度函数提升模型容量
- 适合量子算法设计初学者和研究者快速探索优质电路
量子计算与机器学习的融合催生了量子机器学习（QML）算法的发展，但高性能QML模型的设计需专家经验，成为普及障碍。核心挑战在于数据编码机制与参数化量子电路的设计，二者直接影响模型泛化能力。本文提出一种新方法，将量子电路架构信息编码，利用有效维度作为适应度函数，驱动量子电路设计的进化优化。数值模拟表明，该方法能发现具备更强学习能力的变分量子电路结构，显著提升复杂任务下的QML模型整体性能。
原文摘要 · Abstract (English)
Recent advancements in quantum computing (QC) and machine learning (ML) have garnered significant attention, leading to substantial efforts toward the development of quantum machine learning (QML) algorithms to address a variety of complex challenges. The design of high-performance QML models, however, requires expert-level knowledge, posing a significant barrier to the widespread adoption of QML. Key challenges include the design of data encoding mechanisms and parameterized quantum circuits, both of which critically impact the generalization capabilities of QML models. We propose a novel method that encodes quantum circuit architecture information to enable the evolution of quantum circuit designs. In this approach, the fitness function is based on the effective dimension, allowing for the optimization of quantum circuits towards higher model capacity. Through numerical simulations, we demonstrate that the proposed method is capable of discovering variational quantum circuit architectures that offer improved learning capabilities, thereby enhancing the overall performance of QML models for complex tasks.
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