scholarly journals Provable Advantage in Quantum Phase Learning via Quantum Kernel Alphatron

2022 ◽  
Author(s):  
Wu Yusen ◽  
Bujiao Wu ◽  
Jingbo Wang ◽  
Xiao Yuan
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quantum Phase ◽  
Learning Problems ◽  
Learning Problem ◽  
Quantum Phases ◽  
Speed Up ◽  
Efficient Prediction ◽  
Symmetry Protected

Abstract The use of quantum computation to speed-up machine learning algorithms is among the most exciting prospective applications in the NISQ era. Here, we focus on the quantum phase learning problem, which is crucially important in understanding many-particle quantum systems. We prove that, under widely believed complexity theory assumptions, quantum phase learning problem cannot be efficiently solved by machine learning algorithms using classical resources and classical data. Whereas using quantum data, we prove the universality of quantum kernel Alphatron in efficiently predicting quantum phases, indicating clear quantum advantages in such learning problems. We numerically benchmark the algorithm for a variety of problems, including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in efficient prediction of quantum phases of many-particle systems.

Can user models be learned at all? Inherent problems in machine learning for user modelling

2004 ◽  
Vol 19 (1) ◽  
pp. 61-88 ◽  
Author(s):  
MARTIN E. MÜLLER
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
User Model ◽  
User Modelling ◽  
Machine Learning Algorithms ◽  
Learning Problem ◽  
User Models ◽  
Modelling Techniques ◽  
Modelling Problem

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.

Interconnecting a Class of Machine Learning Algorithms with Logical Commonsense Reasoning Operations

2010 ◽  
pp. 214-246
Author(s):  
Xenia Naidenova
Keyword(s):  
Machine Learning ◽  
Large Class ◽  
Diagnostic Tests ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Problems ◽  
Mathematical Language ◽  
Commonsense Reasoning ◽  
Good Classification ◽  
Logical Rules

The purpose of this chapter is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using well-known deduction and induction logical rules. The concept of a good classification (diagnostic) test for a given set of positive examples lies in the basis of our approach to the machine learning problems. The task of inferring all good diagnostic tests is formulated as searching the best approximations of a given classification (a partitioning) on a given set of examples. The lattice theory is used as a mathematical language for constructing good classification tests. The algorithms of good tests inference are decomposed into subtasks and operations that are in accordance with main human commonsense reasoning rules.

scholarly journals Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Md. Mehedi Hassan ◽  
Md. Mahedi Hassan ◽  
Laboni Akter ◽  
Md. Mushfiqur Rahman ◽  
Sadika Zaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Efficient Prediction

Reducing a Class of Machine Learning Algorithms to Logical Commonsense Reasoning Operations

2011 ◽  
pp. 41-64
Author(s):  
Xenia Naidenova
Keyword(s):  
Machine Learning ◽  
Large Class ◽  
Diagnostic Tests ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Problems ◽  
Mathematical Language ◽  
Commonsense Reasoning ◽  
Good Classification ◽  
Logical Rules

The purpose of this paper is to demonstrate the possibility of transforming a large class of machine learning algorithms into commonsense reasoning processes based on using well-known deduction and induction logical rules. The concept of a good classification (diagnostic) test for a given set of positive examples lies in the basis of our approach to the machine learning problems. The task of inferring all good diagnostic tests is formulated as searching the best approximations of a given classification (a partitioning) on a given set of examples. The lattice theory is used as a mathematical language for constructing good classification tests. The algorithms of good tests inference are decomposed into subtasks and operations that are in accordance with main human commonsense reasoning rules.

Building a DNS Tunneling Dataset

2020 ◽  
Vol 10 ◽  
Author(s):  
Mohammed Al-Drees ◽  
Marwah M. Almasri ◽  
Mousa Al-Akhras ◽  
Mohammed Alawairdhi
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Main Concern ◽  
Domain Name System ◽  
Domain Name ◽  
Ip Address ◽  
Phone Book ◽  
Speed Up

Background:: Domain Name System (DNS) is considered the phone book of the Internet. Its main goal is to translate a domain name to an IP address that the computer can understand. However, DNS can be vulnerable to various kinds of attacks, such as DNS poisoning attacks and DNS tunneling attacks. Objective:: The main objective of this paper is to allow researchers to identify DNS tunnel traffic using machine-learning algorithms. Training machine-learning algorithms to detect DNS tunnel traffic and determine which protocol was used will help the community to speed up the process of detecting such attacks. Method:: In this paper, we consider the DNS tunneling attack. In addition, we discuss how attackers can exploit this protocol to infiltrate data breaches from the network. The attack starts by encoding data inside the DNS queries to the outside of the network. The malicious DNS server will receive the small chunk of data decoding the payload and put it together at the server. The main concern is that the DNS is a fundamental service that is not usually blocked by a firewall and receives less attention from systems administrators due to a vast amount of traffic. Results:: This paper investigates how this type of attack happens using the DNS tunneling tool by setting up an environment consisting of compromised DNS servers and compromised hosts with the Iodine tool installed in both machines. The generated dataset contains the traffic of HTTP, HTTPS, SSH, SFTP, and POP3 protocols over the DNS. No features were removed from the dataset so that researchers could utilize all features in the dataset. Conclusion:: DNS tunneling remains a critical attack that needs more attention to address. DNS tunneled environment allows us to understand how such an attack happens. We built the appropriate dataset by simulating various attack scenarios using different protocols. The created dataset contains PCAP, JSON, and CSV files to allow researchers to use different methods to detect tunnel traffic.

scholarly journals Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nelson Filipe Costa ◽  
Omar Yasser ◽  
Aidar Sultanov ◽  
Gheorghe Sorin Paraoanu
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Adaptive Methods ◽  
Machine Learning Algorithms ◽  
Trapped Ions ◽  
Quantum Phase ◽  
Nitrogen Vacancy ◽  
Phase Estimation ◽  
Fringe Visibility ◽  
Swarm Optimization

AbstractQuantum phase estimation is a paradigmatic problem in quantum sensing and metrology. Here we show that adaptive methods based on classical machine learning algorithms can be used to enhance the precision of quantum phase estimation when noisy non-entangled qubits are used as sensors. We employ the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to this task and we identify the optimal feedback policies which minimize the Holevo variance. We benchmark these schemes with respect to scenarios that include Gaussian and Random Telegraph fluctuations as well as reduced Ramsey-fringe visibility due to decoherence. We discuss their robustness against noise in connection with real experimental setups such as Mach–Zehnder interferometry with optical photons and Ramsey interferometry in trapped ions, superconducting qubits and nitrogen-vacancy (NV) centers in diamond.

scholarly journals Machine Learning Algorithm Application in Software Quality Improvement using Metrics

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1873-1876
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Problems ◽  
Fertile Ground ◽  
Quality Testing ◽  
Software Quality Metrics ◽  
Design And Testing

Machine learning purely concerned on the concept with building the program that improves the tasks performance through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems, analyzing design and testing plays the major role and approached in terms of learning algorithms We discuss several metrics in each of five types of software quality metrics: product quality, in-process quality, testing quality, maintenance equality, and customer satisfaction quality.

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