scholarly journals Clustering and enhanced classification using a hybrid quantum autoencoder

2021 ◽  
Author(s):  
Maiyuren Srikumar ◽  
Charles Daniel Hill ◽  
Lloyd Hollenberg
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Quantum Information Theory ◽  
Quantum States ◽  
Data Sets ◽  
New Paradigm ◽  
Novel Approach ◽  
Representational Space ◽  
Quantum Machine Learning ◽  
Pure States

Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify – and classically represent – their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states – which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

scholarly journals A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Pedro M. Milani ◽  
Julia Ling ◽  
Gonzalo Saez-Mischlich ◽  
Julien Bodart ◽  
John K. Eaton
Keyword(s):  
Machine Learning ◽  
Film Cooling ◽  
Turbulent Heat Flux ◽  
Navier Stokes ◽  
Data Sets ◽  
Turbulent Heat ◽  
Turbulent Diffusivity ◽  
Film Cooling Effectiveness ◽  
Cooling Flows ◽  
Novel Approach

In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynolds-averaged Navier–Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning (ML) algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness.

scholarly journals Computable constraints on entanglement-sharing of multipartite quantum states

2010 ◽  
Vol 10 (3&4) ◽  
pp. 223-238
Author(s):  
Y.-C. Ou ◽  
M.S. Byrd
Keyword(s):  
Information Theory ◽  
Quantum Information ◽  
Quantum Information Theory ◽  
Quantum States ◽  
Many Body ◽  
Pure States ◽  
Arbitrary Dimensions ◽  
Many Body Systems ◽  
Bipartite States ◽  
Entanglement Sharing

\Negativity is regarded as an important measure of entanglement in quantum information theory. In contrast to other measures of entanglement, it is easily computable for bipartite states in arbitrary dimensions. In this paper, based on the negativity and realignment, we provide a set of entanglement-sharing constraints for multipartite states, where the entanglement is not necessarily limited to bipartite and pure states, thus aiding in the quantification of constraints for entanglement-sharing. These may find applications in studying many-body systems.

scholarly journals Power of data in quantum machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hsin-Yuan Huang ◽  
Michael Broughton ◽  
Masoud Mohseni ◽  
Ryan Babbush ◽  
Sergio Boixo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Tolerant ◽  
Quantum Model ◽  
Data Sets ◽  
Learning Models ◽  
Learning Tasks ◽  
Wide Range ◽  
Quantum Machine Learning ◽  
Classical Models ◽  
Near Term

AbstractThe use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits.

scholarly journals AImmune: a new blood-based machine learning approach to improving immune profiling analysis on COVID-19 patients

2021 ◽  
Author(s):  
Xi Tom Zhang ◽  
Runpeng Harris Han
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Mononuclear Cells ◽  
Data Sets ◽  
Learning Approaches ◽  
Rna Seq ◽  
Real World Data ◽  
Novel Approach ◽  
Massive Number ◽  
Immune Profiling

A massive number of transcriptomic profiles of blood samples from COVID-19 patients has been produced since pandemic COVID-19 begins, however, these big data from primary studies have not been well integrated by machine learning approaches. Taking advantage of modern machine learning arthrograms, we integrated and collected single cell RNA-seq (scRNA-seq) data from three independent studies, identified genes potentially available for interpretation of severity, and developed a high-performance deep learning-based deconvolution model AImmune that can predict the proportion of seven different immune cells from the bulk RNA-seq results of human peripheral mononuclear cells. This novel approach which can be used for clinical blood testing of COVID-19 on the ground that previous research shows that mRNA alternations in blood-derived PBMCs may serve as a severity indicator. Assessed on real-world data sets, the AImmune model outperformed the most recognized immune profiling model CIBERSORTx. The presented study showed the results obtained by the true scRNA-seq route can be consistently reproduced through the new approach AImmune, indicating a potential replacing the costly scRNA-seq technique for the analysis of circulating blood cells for both clinical and research purposes.

scholarly journals Discriminating Quantum States with Quantum Machine Learning

2021 ◽  
Author(s):  
David Quiroga ◽  
Prasanna Date ◽  
Raphael Pooser
Keyword(s):  
Machine Learning ◽  
Quantum States ◽  
Quantum Machine Learning

scholarly journals Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets

2021 ◽  
Vol 34 (2) ◽  
pp. 541-549 ◽  
Author(s):  
Leihong Wu ◽  
Ruili Huang ◽  
Igor V. Tetko ◽  
Zhonghua Xia ◽  
Joshua Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Sets ◽  
Predictive Toxicology ◽  
Trade Off

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Robust in practice: Adversarial attacks on quantum machine learning

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Haoran Liao ◽  
Ian Convy ◽  
William J. Huggins ◽  
K. Birgitta Whaley
Keyword(s):  
Machine Learning ◽  
Quantum Machine Learning
Sign in / Sign up

Export Citation Format

Share Document