Mach. Learn. Sci. Technol.最新文献

Transforming two-dimensional tensor networks into quantum circuits for supervised learning 将二维张量网络转化为用于监督学习的量子电路

Mach. Learn. Sci. Technol. Pub Date : 2024-03-04 DOI: 10.1088/2632-2153/ad2fec

Zhihui Song, Jinchen Xu, Xin Zhou, X. Ding, Zheng Shan

{"title":"Transforming two-dimensional tensor networks into quantum circuits for supervised learning","authors":"Zhihui Song, Jinchen Xu, Xin Zhou, X. Ding, Zheng Shan","doi":"10.1088/2632-2153/ad2fec","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2fec","url":null,"abstract":"\u0000 There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network states in classical tensor network machine learning, it is promising to explore whether its application in quantum machine learning can extend the performance boundary of the models. Here, we transform two-dimensional tensor networks into quantum circuits for supervised learning. Specifically, we encode two-dimensional tensor networks into quantum circuits through rigorous mathematical proofs for constructing model ansätze, including string-bond states, entangled-plaquette states and isometric tensor network states. In addition, we propose adaptive data encoding methods and combine with tensor networks. We construct a tensor-network-inspired quantum circuit supervised learning framework for transferring tensor network machine learning from classical to quantum, and build several novel two-dimensional tensor network-inspired quantum classifiers based on this framework. Finally, we propose a parallel quantum machine learning method for multi-class classification to construct 2D TNQC-based multi-class classifiers. Classical simulation results on the MNIST benchmark dataset show that our proposed models achieve the state-of-the-art accuracy performance, significantly outperforming other quantum classifiers on both binary and multi-class classification tasks, and beat simple convolutional classifiers on a fair track with identical inputs. The noise resilience of the models makes them successfully run and work in a real quantum computer.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"23 8","pages":"15048"},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis 更精确和有效的光谱泛函拟合的先验传播及其在铁电迟滞中的应用

Mach. Learn. Sci. Technol. Pub Date : 2021-04-26 DOI: 10.1088/2632-2153/ABFBBA

N. Creange, K. P. Kelley, C. Smith, D. Sando, O. Paull, N. Valanoor, S. Somnath, S. Jesse, S. Kalinin, R. Vasudevan

引用次数: 2

Neural network analysis of neutron and x-ray reflectivity data: pathological cases, performance and perspectives 中子和x射线反射率数据的神经网络分析:病理病例、表现和观点

Mach. Learn. Sci. Technol. Pub Date : 2021-04-20 DOI: 10.1088/2632-2153/ABF9B1

Alessandro Greco, V. Starostin, A. Hinderhofer, A. Gerlach, M. Skoda, S. Kowarik, F. Schreiber

引用次数: 9

Advances in scientific literature mining for interpreting materials characterization 用于材料表征解释的科学文献挖掘研究进展

Mach. Learn. Sci. Technol. Pub Date : 2021-04-13 DOI: 10.1088/2632-2153/ABF751

Gilchan Park, Line C. Pouchard

引用次数: 1

Phases of learning dynamics in artificial neural networks in the absence or presence of mislabeled data 在没有或存在错误标记数据的情况下，人工神经网络中学习动态的阶段

Mach. Learn. Sci. Technol. Pub Date : 2021-04-07 DOI: 10.1088/2632-2153/ABF5B9

Yu Feng, Y. Tu

引用次数: 12

MPGVAE: improved generation of small organic molecules using message passing neural nets MPGVAE:利用信息传递神经网络改进小有机分子的生成

Mach. Learn. Sci. Technol. Pub Date : 2021-04-07 DOI: 10.1088/2632-2153/ABF5B7

Daniel Flam-Shepherd, Tony C Wu, Alán Aspuru-Guzik

引用次数: 14

COVID-19 detection from lung CT-scan images using transfer learning approach 基于迁移学习方法的肺部ct扫描图像COVID-19检测

Mach. Learn. Sci. Technol. Pub Date : 2021-03-25 DOI: 10.1088/2632-2153/ABF22C

A. Halder, B. Datta

{"title":"COVID-19 detection from lung CT-scan images using transfer learning approach","authors":"A. Halder, B. Datta","doi":"10.1088/2632-2153/ABF22C","DOIUrl":"https://doi.org/10.1088/2632-2153/ABF22C","url":null,"abstract":"Since the onset of 2020, the spread of coronavirus disease (COVID-19) has rapidly accelerated worldwide into a state of severe pandemic. COVID-19 has infected more than 29 million people and caused more than 900 thousand deaths at the time of writing. Since it is highly contagious, it causes explosive community transmission. Thus, health care delivery has been disrupted and compromised by the lack of testing kits. COVID-19-infected patients show severe acute respiratory syndrome. Meanwhile, the scientific community has been involved in the implementation of deep learning (DL) techniques to diagnose COVID-19 using computed tomography (CT) lung scans, since CT is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. However, large datasets of CT-scan images are not publicly available due to privacy concerns and obtaining very accurate models has become difficult. Thus, to overcome this drawback, transfer-learning pre-trained models are used in the proposed methodology to classify COVID-19 (positive) and COVID-19 (negative) patients. We describe the development of a DL framework that includes pre-trained models (DenseNet201, VGG16, ResNet50V2, and MobileNet) as its backbone, known as KarNet. To extensively test and analyze the framework, each model was trained on original (i.e. unaugmented) and manipulated (i.e. augmented) datasets. Among the four pre-trained models of KarNet, the one that used DenseNet201 demonstrated excellent diagnostic ability, with AUC scores of 1.00 and 0.99 for models trained on unaugmented and augmented data sets, respectively. Even after considerable distortion of the images (i.e. the augmented dataset) DenseNet201 achieved an accuracy of 97% for the test dataset, followed by ResNet50V2, MobileNet, and VGG16 (which achieved accuracies of 96%, 95%, and 94%, respectively).","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"18 1","pages":"45013"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76476231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Machine learning inference of molecular dipole moment in liquid water 液态水分子偶极矩的机器学习推理

Mach. Learn. Sci. Technol. Pub Date : 2021-03-15 DOI: 10.1088/2632-2153/ac0123

L. Knijff, Chao Zhang

引用次数: 2

Anomaly detection in gravitational waves data using convolutional autoencoders 基于卷积自编码器的引力波数据异常检测

Mach. Learn. Sci. Technol. Pub Date : 2021-03-13 DOI: 10.1088/2632-2153/abf3d0

F. Morawski, M. Bejger, E. Cuoco, Luigia Petre

{"title":"Anomaly detection in gravitational waves data using convolutional autoencoders","authors":"F. Morawski, M. Bejger, E. Cuoco, Luigia Petre","doi":"10.1088/2632-2153/abf3d0","DOIUrl":"https://doi.org/10.1088/2632-2153/abf3d0","url":null,"abstract":"As of this moment, fifty gravitational waves (GW) detections have been announced, thanks to the observational efforts of the LIGO-Virgo Collaboration, working with the Advanced LIGO and the Advanced Virgo interferometers. The detection of signals is complicated by the noise-dominated nature of the data. Conventional approaches in GW detection procedures require either precise knowledge of the GW waveform in the context of matched filtering searches or coincident analysis of data from multiple detectors. Furthermore, the analysis is prone to contamination by instrumental or environmental artifacts called glitches which either mimic astrophysical signals or reduce the overall quality of data. In this paper, we propose an alternative generic method of studying GW data based on detecting anomalies. The anomalies we study are transient signals, different from the slow non-stationary noise of the detector. Presented in the manuscript anomalies are mostly based on the GW emitted by the mergers of binary black hole systems. However, the presented study of anomalies is not limited only to GW alone, but also includes glitches occurring in the real LIGO/Virgo dataset available at the Gravitational Waves Open Science Center.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"10 1","pages":"45014"},"PeriodicalIF":0.0,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90176678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Multi-scale tensor network architecture for machine learning 用于机器学习的多尺度张量网络架构

Mach. Learn. Sci. Technol. Pub Date : 2021-01-01 DOI: 10.1088/2632-2153/abffe8

J. Reyes, E. Stoudenmire

引用次数: 21