Paper Reading AI Learner

Quantum-enhanced causal discovery for a small number of samples

2025-01-09 07:05:22
Yota Maeda, Ken Arai, Yu Tanaka, Yu Terada, Hiroshi Ueno, Hiroyuki Tezuka

Abstract

The discovery of causal relationships from observed data has attracted significant interest from disciplines such as economics, social sciences, epidemiology, and biology. In practical applications, considerable knowledge of the underlying systems is often unavailable, and real data are often associated with nonlinear causal structures, which make the direct use of most conventional causality analysis methods difficult. This study proposes a novel quantum Peter-Clark (qPC) algorithm for causal discovery that does not assume any underlying model structures. Based on the independence conditional tests in a class of reproducing kernel Hilbert spaces characterized by quantum circuits, the proposed qPC algorithm can explore causal relationships from the observed data drawn from arbitrary distributions. We conducted systematic experiments on fundamental graph parts of causal structures, demonstrating that the qPC algorithm exhibits a significantly better performance, particularly with smaller sample sizes compared to its classical counterpart. Furthermore, we proposed a novel optimization approach based on Kernel Target Alignment (KTA) for determining hyperparameters of quantum kernels. This method effectively reduced the risk of false positives in causal discovery, enabling more reliable inference. Our theoretical and experimental results demonstrate that the proposed quantum algorithm can empower classical algorithms for robust and accurate inference in causal discovery, supporting them in regimes where classical algorithms typically fail. Additionally, the effectiveness of this method was validated using the Boston Housing dataset as a real-world application. These findings demonstrate the new potential of quantum circuit-based causal discovery methods in addressing practical challenges, particularly in small-sample scenarios where traditional approaches have shown limitations.

Abstract (translated)

从观测数据中发现因果关系已经吸引了经济学、社会科学、流行病学和生物学等学科的广泛关注。在实际应用中,通常缺乏对底层系统的充分了解,并且真实数据常常与非线性因果结构相关联,这使得大多数传统因果分析方法难以直接使用。本研究提出了一种新颖的量子Peter-Clark (qPC) 算法用于因果发现,该算法不假设任何底层模型结构。基于一类由量子电路表征的再生核希尔伯特空间中的独立性条件测试,所提出的 qPC 算法能够从任意分布中抽取的数据探索因果关系。我们在基本图形部分的因果结构上进行了系统的实验,结果表明,与经典方法相比,qPC算法在样本量较小的情况下表现出了显著更好的性能。此外,我们还提出了一种基于核目标对齐(KTA)的新优化方法来确定量子核的超参数,这种方法有效降低了因果发现中假阳性的风险,从而实现了更为可靠的推断。我们的理论和实验结果表明,所提出的量子算法能够增强经典算法在因果发现中的稳健性和准确性,使其能够在传统算法通常失败的情况下发挥作用。此外,我们使用波士顿住房数据集作为实际应用案例验证了该方法的有效性。这些发现展示了基于量子电路的因果发现方法的新潜力,在小样本场景等实践中解决挑战,并且这种方法在传统的技术手段显示局限性的场合尤其有效。

URL

https://arxiv.org/abs/2501.05007

PDF

https://arxiv.org/pdf/2501.05007.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot