用并行退火训练量子玻尔兹曼机，提升医疗图像分类效率
Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
- 提出改进的并行退火方法，用于监督式量子玻尔兹曼机训练
- 在MedMNIST数据集上达到与小型CNN相当的准确率，训练轮次减少
- 相比传统退火，速度提升近70%，更适配当前量子硬件
基于量子退火采样天然服从玻尔兹曼分布的特性，退火型量子玻尔兹曼机（QBM）在量子研究领域日益受到关注。尽管具有潜在量子加速优势，其训练仍需大量量子处理单元（QPU）时间，限制了在当前噪声中等规模量子（NISQ）时代的应用。受Noè等（2024）启发，本文提出一种改进的并行量子退火方法，应用于监督式QBM训练。通过节省编码输入的量子比特，该方法可在MedMNIST数据集（Yang et al., 2023）上测试，推动技术向真实场景应用迈进。实验表明，使用该方法的QBM已达到与同规模卷积神经网络（CNN）相当的性能，且训练轮次显著更少。相比常规退火执行，本方法实现近70%的速度提升。
原文摘要 · Abstract (English)
Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increasing popularity in the quantum research community. While they harbor great promises for quantum speed-up, their usage currently stays a costly endeavor, as large amounts of QPU time are required to train them. This limits their applicability in the NISQ era. Following the idea of Noè et al. (2024), who tried to alleviate this cost by incorporating parallel quantum annealing into their unsupervised training of QBMs, this paper presents an improved version of parallel quantum annealing that we employ to train QBMs in a supervised setting. Saving qubits to encode the inputs, the latter setting allows us to test our approach on medical images from the MedMNIST data set (Yang et al., 2023), thereby moving closer to real-world applicability of the technology. Our experiments show that QBMs using our approach already achieve reasonable results, comparable to those of similarly-sized Convolutional Neural Networks (CNNs), with markedly smaller numbers of epochs than these classical models. Our parallel annealing technique leads to a speed-up of almost 70 % compared to regular annealing-based BM executions.
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