对比经典与量子机器学习估算活动星系核黑洞质量，发现经典模型更优。
Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches
- 用多种经典与量子算法比较黑洞质量预测性能。
- LSTM模型准确率达99.77%，优于所有量子模型。
- 首次系统评估量子算法在天体物理数据中的应用潜力。
对于类型2活动星系核（Type-2 AGNs），黑洞质量估计极具挑战性。理解星系形成与演化需要精确掌握黑洞质量。本研究对比了多种经典与量子机器学习（QML）算法在黑洞质量估计中的表现，经典算法包括线性回归、XGBoost、随机森林、支持向量回归（SVR）、Lasso、Ridge、Elastic Net、贝叶斯回归、决策树、梯度提升、经典神经网络、门控循环单元（GRU）、LSTM、深度残差网络（ResNets）及基于Transformer的回归模型。量子算法则涵盖混合量子神经网络（QNN）、量子长短期记忆（Q-LSTM）、采样器-QNN、估测器-QNN、变分量子回归（VQR）、量子线性回归（Q-LR）及使用JAX优化的QML。结果表明，经典算法在决定系数R²、平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE）上均优于量子模型。其中，LSTM表现最佳，准确率达99.77%；估测器-QNN在量子模型中表现最优，MSE为0.0124，准确率为99.75%。本研究明确了经典与量子方法的优劣，据我们所知，为未来量子算法在天体物理数据分析中的应用提供了基础。
原文摘要 · Abstract (English)
In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black holes. This work compared different classical and quantum machine learning (QML) algorithms for black hole mass estimation, wherein the classical algorithms are Linear Regression, XGBoost Regression, Random Forest Regressor, Support Vector Regressor (SVR), Lasso Regression, Ridge Regression, Elastic Net Regression, Bayesian Regression, Decision Tree Regressor, Gradient Booster Regressor, Classical Neural Networks, Gated Recurrent Unit (GRU), LSTM, Deep Residual Networks (ResNets) and Transformer-Based Regression. On the other hand, quantum algorithms including Hybrid Quantum Neural Networks (QNN), Quantum Long Short-Term Memory (Q-LSTM), Sampler-QNN, Estimator-QNN, Variational Quantum Regressor (VQR), Quantum Linear Regression(Q-LR), QML with JAX optimization were also tested. The results revealed that classical algorithms gave better R^2, MAE, MSE, and RMSE results than the quantum models. Among the classical models, LSTM has the best result with an accuracy of 99.77%. Estimator-QNN has the highest accuracy for quantum algorithms with an MSE of 0.0124 and an accuracy of 99.75%. This study ascertains both the strengths and weaknesses of the classical and the quantum approaches. As far as our knowledge goes, this work could pave the way for the future application of quantum algorithms in astrophysical data analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。