佛罗里达大西洋大学研究人员在小训练数据条件下，用基于门型和退火型的量子机器学习方法进行肺癌预测研究，论文发表于 Springer Nature 旗下 Discover Quantum Science。其中基于 D-Wave 技术实现的量子支持向量机表现最佳，仿真中准确率达 94.31%，在 D-Wave Advantage 退火量子计算机上达 91.90%。
AI 生成摘要 · 以原文为准
Developing reliable machine learning models for healthcare can be challenging when only small datasets are available.
Researchers at @FloridaAtlantic University’s College of Engineering and Computer Science (@fauengineering) evaluated quantum machine learning approaches in simulation and on quantum hardware using symptom and risk-factor data for lung cancer prediction under limited training data.
The study, published in Discover Quantum Science, a @Springer Nature journal, “Inferring lung cancer under limited learning data through gate-based and annealing-based quantum techniques,” provides a common framework for evaluating different quantum machine learning methods under the same data preparation and testing conditions.
The best-performing configuration, a quantum support vector machine implemented using D-Wave's technology, achieved 94.31% accuracy in simulation and 91.90% on D-Wave’s Advantage™ annealing quantum computers. The findings highlight the potential of annealing quantum computing to advance AI and machine learning in data-constrained environments.
Learn more: https://www.dwavequantum.com/company/newsroom/press-release/fau-researchers-explore-quantum-computing-to-predict-lung-cancer-with-limited-data/
#QuantumComputing #QuantumMachineLearning #HealthcareInnovation
来源：D-Wave · x.com