scholarly journals Design and Implementation of Quantum half Adder and Full adder Using IBM Quantum Experience

2021 ◽  
Vol 10 (2) ◽  
pp. 964-967
Keyword(s):  
Machine Learning ◽  
Circuit Design ◽  
Full Adder ◽  
Quantum Computers ◽  
Computational Power ◽  
Classical Systems ◽  
Learning Tasks ◽  
Huge Data ◽  
Half Adder ◽  
Future Events

Quantum machine learning is the combination of quantum computing and classical machine learning. It helps in solving the problems of one field to another field. Quantum computational power can be advantageous in handling huge data at a faster rate.In this regard, quantum computational power can be advantageous in handling such huge data at a faster rate. Classical machine learning is about trying to find patterns in data and using those patterns to predict future events. Quantum systems, on the other hand, produces typical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Hence the whole motivation in this work is on understanding and analysing the half adder and full adder circuit design using Quantum mechanics.

scholarly journals A Simple Ensemble Learning Knowledge Distillation

2020 ◽  
Author(s):  
Himel Das Gupta ◽  
Kun Zhang ◽  
Victor S. Sheng
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Performance Enhancement ◽  
Portable Devices ◽  
Computational Power ◽  
Small Machine ◽  
Learning Tasks ◽  
Machine Learning Applications ◽  
Knowledge Distillation ◽  
Small Models

Deep neural network (DNN) has shown significant improvement in learning and generalizing different machine learning tasks over the years. But it comes with an expense of heavy computational power and memory requirements. We can see that machine learning applications are even running in portable devices like mobiles and embedded systems nowadays, which generally have limited resources regarding computational power and memory and thus can only run small machine learning models. However, smaller networks usually do not perform very well. In this paper, we have implemented a simple ensemble learning based knowledge distillation network to improve the accuracy of such small models. Our experimental results prove that the performance enhancement of smaller models can be achieved through distilling knowledge from a combination of small models rather than using a cumbersome model for the knowledge transfer. Besides, the ensemble knowledge distillation network is simpler, time-efficient, and easy to implement.

scholarly journals Machine Learning: A Quantum Perspective

2021 ◽  
Author(s):  
Aishwarya Jhanwar ◽  
Manisha J. Nene
Keyword(s):  
Machine Learning ◽  
Quantum Mechanics ◽  
Quantum Computing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quantum Computers ◽  
Learning Tasks ◽  
Current Scenario ◽  
Chip Fabrication ◽  
Classical Computing

Recently, increased availability of the data has led to advances in the field of machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. Since quantum computers leverages the properties of quantum mechanics, it carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. This paper addresses the concepts of quantum computing which influences machine learning in a quantum world. It also states the speedup observed in different machine learning algorithms when executed on quantum computers. The paper towards the end advocates the use of quantum application software and throw light on the existing challenges faced by quantum computers in the current scenario.

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 Survey of NISQ Era Hybrid Quantum-Classical Machine Learning Research

2021 ◽  
Author(s):  
Gennaro De Luca
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
Cloud Services ◽  
Quantum Computers ◽  
Intermediate Scale ◽  
Classical Systems ◽  
The Past ◽  
Introductory Overview ◽  
Learning Research ◽  
Language Environment

Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia. Several physical quantum computers are now freely available to use through cloud services, with some implementations supporting upwards of hundreds of qubits. These advances mark the beginning of the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, paving the way for hybrid quantum-classical systems. This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to hybrid quantum-classical machine learning.

Si Nanodot Device Fabricated by Thermal Oxidation and their Applications

2011 ◽  
Vol 470 ◽  
pp. 175-183
Author(s):  
Yasuo Takahashi ◽  
Ming Yu Jo ◽  
Takuya Kaizawa ◽  
Yuki Kato ◽  
Masashi Arita ◽  
...  
Keyword(s):  
Full Adder ◽  
Silicon On Insulator ◽  
Single Electron ◽  
Quantum Computers ◽  
Electrical Characteristics ◽  
Easy Method ◽  
Half Adder ◽  
Single Electron Devices ◽  
The Wire ◽  
New Applications

Small single-electron devices (SEDs) consisting of many Si nanodots are fabricated on a silicon-on-insulator (SOI) wafer by means of pattern-dependent oxidation (PADOX) method. We investigated SEDs from two kinds of viewpoint. One is how to fabricate the nanodots, especially coupled nanodots, which are important to achieve quantum computers and single-electron transfer devices. The other is demonstration of new applications that tolerate the size fluctuation. In order to achieve multi-coupled nanodots, we developed an easy method by applying PADOX to a specially designed Si nanowire which has small constrictions at the ends of the wire. We confirmed the double-dot formation and position of the Si nanodots in the wire by analyzing the measured electrical characteristics. To achieve high functionality together with low-power consumption and tolerance to size fluctuation, we developed nanodot array device which has many input gates and outputs terminals. The fabricated three-input and two-output nanodot device actually provide high functionality such as a half adder and a full adder.

scholarly journals A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

2021 ◽  
Vol 18 (4) ◽  
pp. 2121
Author(s):  
Mansoureh Maadi ◽  
Hadi Akbarzadeh Khorshidi ◽  
Uwe Aickelin
Keyword(s):  
Machine Learning ◽  
Future Research ◽  
Computational Power ◽  
Medical Field ◽  
Interactive Machine Learning ◽  
Human In The Loop ◽  
Human Interactions ◽  
Scoping Literature Review ◽  
Domain Expertise ◽  
Expertise Level

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

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 Utilising Flow Aggregation to Classify Benign Imitating Attacks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1761
Author(s):  
Hanan Hindy ◽  
Robert Atkinson ◽  
Christos Tachtatzis ◽  
Ethan Bayne ◽  
Miroslav Bures ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Network Traffic ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Computational Power ◽  
Human Understanding ◽  
Flow Aggregation ◽  
Additional Level ◽  
Attack Surfaces

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

scholarly journals QUBO formulations for training machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prasanna Date ◽  
Davis Arthur ◽  
Lauren Pusey-Nazzaro
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Large Scale ◽  
Support Vector ◽  
Quantum Computers ◽  
Np Hard ◽  
Learning Models ◽  
Moore’S Law ◽  
Moore's Law ◽  
Machine Learning Models

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

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