用粒子群优化训练量子电路，突破梯度消失瓶颈。
Training Variational Quantum Circuits Using Particle Swarm Optimization
- 采用粒子群算法优化量子门选择与参数，避免梯度消失
- 在MedMNIST数据集上达到媲美甚至超越传统方法的准确率
- 仅用更少量子门实现高性能，适合资源受限场景
本文采用粒子群优化（PSO）算法训练各类变分量子电路（VQC），以应对梯度下降法常遭遇的“平庸高原”问题。PSO是一种受鸟群集体行为启发的随机优化方法，可调节种群规模、迭代次数及可训练参数数量。本方法对整个VQC结构进行优化，自主选择量子门类型（Rx、Ry、Rz、CNOT）、作用目标量子比特及旋转角度。实验在MedMNIST生物医学图像分类数据集上进行，结果表明，尽管使用的量子门数量少于基于梯度下降的预定义VQC，PSO仍能实现相当或更高的分类准确率。
原文摘要 · Abstract (English)
In this work, the Particle Swarm Optimization (PSO) algorithm has been used to train various Variational Quantum Circuits (VQCs). This approach is motivated by the fact that commonly used gradient-based optimization methods can suffer from the barren plateaus problem. PSO is a stochastic optimization technique inspired by the collective behavior of a swarm of birds. The dimension of the swarm, the number of iterations of the algorithm, and the number of trainable parameters can be set. In this study, PSO has been used to train the entire structure of VQCs, allowing it to select which quantum gates to apply, the target qubits, and the rotation angle, in case a rotation is chosen. The algorithm is restricted to choosing from four types of gates: Rx, Ry, Rz, and CNOT. The proposed optimization approach has been tested on various datasets of the MedMNIST, which is a collection of biomedical image datasets designed for image classification tasks. Performance has been compared with the results achieved by classical stochastic gradient descent applied to a predefined VQC. The results show that the PSO can achieve comparable or even better classification accuracy across multiple datasets, despite the PSO using a lower number of quantum gates than the VQC used with gradient descent optimization.
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