scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

scholarly journals A New Model Averaging Approach in Predicting Credit Risk Default

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 114
Author(s):  
Paritosh Navinchandra Jha ◽  
Marco Cucculelli
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Model Averaging ◽  
Research Direction ◽  
Future Research ◽  
Classification Problems ◽  
Average Technique ◽  
Model Average ◽  
Novel Approach ◽  
Unbalanced Dataset

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

scholarly journals QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers

2022 ◽  
Vol 9 ◽  
Author(s):  
Mahabubul Alam ◽  
Swaroop Ghosh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Architecture ◽  
Empirical Studies ◽  
Quantum Computers ◽  
Quantum Neural Network ◽  
Small Quantum ◽  
Conventional Machine ◽  
Near Term ◽  
The Impact

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks. Existing QML models that use deep parametric quantum circuits (PQC) suffer from a large accumulation of gate errors and decoherence. To circumvent this issue, we propose a new QML architecture called QNet. QNet consists of several small quantum neural networks (QNN). Each of these smaller QNN’s can be executed on small quantum computers that dominate the NISQ-era machines. By carefully choosing the size of these QNN’s, QNet can exploit arbitrary size quantum computers to solve supervised ML tasks of any scale. It also enables heterogeneous technology integration in a single QML application. Through empirical studies, we show the trainability and generalization of QNet and the impact of various configurable variables on its performance. We compare QNet performance against existing models and discuss potential issues and design considerations. In our study, we show 43% better accuracy on average over the existing models on noisy quantum hardware emulators. More importantly, QNet provides a blueprint to build noise-resilient QML models with a collection of small quantum neural networks with near-term noisy quantum devices.

scholarly journals Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laszlo Gyongyosi ◽  
Sandor Imre
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Quantum Memory ◽  
Quantum Computers ◽  
Signal To Noise ◽  
Quantum Devices ◽  
Retrieval Efficiency ◽  
Quantum Machine Learning ◽  
Near Term ◽  
Quantum Internet

AbstractQuantum memories are a fundamental of any global-scale quantum Internet, high-performance quantum networking and near-term quantum computers. A main problem of quantum memories is the low retrieval efficiency of the quantum systems from the quantum registers of the quantum memory. Here, we define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory for near-term quantum devices. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure and utilizes the advanced techniques of quantum machine learning. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output signal-to-noise ratio values. We prove that the local unitaries of an HRE quantum memory achieve the optimization of the readout procedure in an unsupervised manner without the use of any labeled data or training sequences. We show that the readout procedure of an HRE quantum memory is realized in a completely blind manner without any information about the input quantum system or about the unknown quantum operation of the quantum register. We evaluate the retrieval efficiency of an HRE quantum memory and the output SNR (signal-to-noise ratio). The results are particularly convenient for gate-model quantum computers and the near-term quantum devices of the quantum Internet.

scholarly journals When Machine Learning Meets Privacy

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Bo Liu ◽  
Ming Ding ◽  
Sina Shaham ◽  
Wenny Rahayu ◽  
Farhad Farokhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Privacy Protection ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Research Progress ◽  
Future Research ◽  
Surveillance Systems ◽  
Smart Healthcare ◽  
Wide Range ◽  
Depth Analysis

The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.

scholarly journals Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 71
Author(s):  
Frank Phillipson ◽  
Robert S. Wezeman ◽  
Irina Chiscop
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Communication Networks ◽  
Mobile Networks ◽  
Quantum Computers ◽  
Limiting Factor ◽  
Learning Approaches ◽  
Specific Task ◽  
Learning Tasks ◽  
Quantum Machine Learning

Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available.

scholarly journals A Survey On Missing Data in Machine Learning

2021 ◽  
Author(s):  
Tlamelo Emmanuel ◽  
Thabiso Maupong ◽  
Dimane Mpoeleng ◽  
Thabo Semong ◽  
Mphago Banyatsang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Human Error ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Research Direction ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approaches ◽  
K Nearest Neighbor

Abstract Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur as a result of various factors like missing completely at random, missing at random or missing not at random. All these may be as a result of system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for. Finally, we experiment on the K nearest neighbor and random forest imputation techniques on novel power plant induced fan data and offer some possible future research direction.

Neuromarketing Trends and Opportunities for Companies

2020 ◽  
pp. 82-103
Author(s):  
Arabinda Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Decision Making ◽  
Brand Management ◽  
Technological Development ◽  
Research Direction ◽  
Future Research ◽  
Design Decision ◽  
Future Research Direction

The main purpose of this chapter is to concisely describe the origin of neuromarketing, its applications in the organization, and to explore consumer behavior with the help of different neuromarketing technologies like fMRI, EEG, and MEG. This chapter gives a guideline on how neuromarketing would be used in different areas of organization functions, like, brand management, advertisement, communication, product design, decision making, etc. with the help of data mining, artificial intelligence, social media, machine learning, remote sensing, AR, and VR. The chapter identifies the opportunities of neuromarketing with the latest technological development to understand the customer mindset so that it would be easy to formulate neurostrategy for an organization. This chapter gives a future research direction with strategic management, so that it will be helpful for a professional to create a more accurate strategy in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, predict, and fulfill the “institution void” situation with more accuracy in an emerging developing market.

scholarly journals Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption

2020 ◽  
Vol 34 (04) ◽  
pp. 3866-3873
Author(s):  
Peter Fenner ◽  
Edward Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Modular Approach ◽  
Multiple Sources ◽  
Machine Learning Model ◽  
Evaluation Of Data

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Fully homomorphic encryption (FHE) allows data to be computed on whilst encrypted, which can provide a solution to the problem of data privacy. However, FHE is both slow and restrictive, so existing algorithms must be manipulated to make them work efficiently under the FHE paradigm. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE and cannot be manipulated to work both efficiently and accurately. In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party's data, without either party gaining access to the other's data, in a way which is both accurate and efficient. This construction is, to our knowledge, the first example of an effectively encrypted Gaussian process.

scholarly journals BACombo—Bandwidth-Aware Decentralized Federated Learning

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 440 ◽  
Author(s):  
Jingyan Jiang ◽  
Liang Hu ◽  
Chenghao Hu ◽  
Jiate Liu ◽  
Zhi Wang
Keyword(s):  
Data Privacy ◽  
Original Data ◽  
Privacy And Security ◽  
Training Time ◽  
Distributed Training ◽  
Communication Time ◽  
Worker Selection ◽  
Learning Scenarios ◽  
Server Design ◽  
Model Segment

The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to efficiently utilize network capacities between computing nodes is crucial for conventional federated learning. In this paper, we propose Bandwidth Aware Combo (BACombo), a model segment level decentralized federated learning, to tackle this problem. In BACombo, we propose a segmented gossip aggregation mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, a bandwidth-aware worker selection model further reduces the transmission delay by greedily choosing the bandwidth-sufficient worker. The convergence guarantees are provided for BACombo. The experimental results on various datasets demonstrate that the training time is reduced by up to 18 times that of baselines without accuracy degrade.

scholarly journals Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 909
Author(s):  
Qian Lv ◽  
Xiaoling Yu ◽  
Haihui Ma ◽  
Junchao Ye ◽  
Weifeng Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Research Direction ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Reciprocating Compressor ◽  
Operating Condition ◽  
Practical Applications ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed.

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