scholarly journals A strategy for quantum algorithm design assisted by machine learning

2014 ◽  
Vol 16 (7) ◽  
pp. 073017 ◽  
Author(s):  
Jeongho Bang ◽  
Junghee Ryu ◽  
Seokwon Yoo ◽  
Marcin Pawłowski ◽  
Jinhyoung Lee
Keyword(s):  
Machine Learning ◽  
Algorithm Design ◽  
Quantum Algorithm

A survey on prediction of diabetes using classification algorithms

2021 ◽  
Vol 2 (104) ◽  
pp. 77-84
Author(s):  
A. Khanwalkar ◽  
R. Soni
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Learning Algorithm ◽  
Algorithm Design ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Collection Method ◽  
Data Collection Method ◽  
Diagnostic Center

Purpose: Diabetes is a chronic disease that pays for a large proportion of the nation's healthcare expenses when people with diabetes want medical care continuously. Several complications will occur if the polymer disorder is not treated and unrecognizable. The prescribed condition leads to a diagnostic center and a doctor's intention. One of the real-world subjects essential is to find the first phase of the polytechnic. In this work, basically a survey that has been analyzed in several parameters within the poly-infected disorder diagnosis. It resembles the classification algorithms of data collection that plays an important role in the data collection method. Automation of polygenic disorder analysis, as well as another machine learning algorithm. Design/methodology/approach: This paper provides extensive surveys of different analogies which have been used for the analysis of medical data, For the purpose of early detection of polygenic disorder. This paper takes into consideration methods such as J48, CART, SVMs and KNN square, this paper also conducts a formal surveying of all the studies, and provides a conclusion at the end. Findings: This surveying has been analyzed on several parameters within the poly-infected disorder diagnosis. It resembles that the classification algorithms of data collection plays an important role in the data collection method in Automation of polygenic disorder analysis, as well as another machine learning algorithm. Practical implications: This paper will help future researchers in the field of Healthcare, specifically in the domain of diabetes, to understand differences between classification algorithms. Originality/value: This paper will help in comparing machine learning algorithms by going through results and selecting the appropriate approach based on requirements.

Reinforcement-Learning-Based Quantum Adiabatic Algorithm Design for Integer Programming

SPIN ◽  
2021 ◽  
Author(s):  
Jiawei Zhu
Keyword(s):  
Quantum Computing ◽  
Reinforcement Learning ◽  
Integer Programming ◽  
Learning Process ◽  
Optimization Problems ◽  
Algorithm Design ◽  
Hamiltonian Path ◽  
Quantum Algorithm ◽  
Learning Approach ◽  
Computation Efficiency

Adiabatic quantum computing (AQC) is a computation protocol to solve difficult problems exploiting quantum advantage, directly applicable to optimization problems. In performing the AQC, different configurations of the Hamiltonian path could lead to dramatic differences in the computation efficiency. It is thus crucial to configure the Hamiltonian path to optimize the computation performance of AQC. Here we apply a reinforcement learning approach to configure AQC for integer programming, where we find the learning process automatically converges to a quantum algorithm that exhibits scaling advantage over the trivial AQC using a linear Hamiltonian path. This reinforcement-learning-based approach for quantum adiabatic algorithm design for integer programming can well be adapted to the quantum resources in different quantum computation devices, due to its built-in flexibility.

Programming Drills with a Decision Trees Workbench

2008 ◽  
pp. 108-120
Author(s):  
Dimitris Kalles ◽  
Athanasios Pagagelis
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Computer Science ◽  
Decision Trees ◽  
Algorithm Design ◽  
Science Students ◽  
Learning Paradigm ◽  
Tree Construction ◽  
Expert User ◽  
Tree Algorithms

Decision trees are one of the most successful Machine Learning paradigms. This paper presents a library of decision tree algorithms in Java that was eventually used as a programming laboratory workbench. The initial design focus was, as regards the non-expert user, to conduct experiments with decision trees using components and visual tools that facilitate tree construction and manipulation and as regards the expert user, to be able to focus on algorithm design and comparison with few implementation details. The system has been built over a number of years and over various development contexts and has been successfully used as a workbench in a programming laboratory for junior computer science students. The underlying philosophy was to achieve a solid introduction to object-oriented concepts and practices based on a fundamental machine learning paradigm.

Quantum algorithm design using dynamic learning

2008 ◽  
Vol 8 (1&2) ◽  
pp. 12-29
Author(s):  
E.C. Behrman ◽  
J.E. Steck ◽  
P. Kumar ◽  
K.A. Walsh
Keyword(s):  
Quantum Interference ◽  
Quantum Computer ◽  
Learning Algorithm ◽  
Algorithm Design ◽  
Quantum Algorithm ◽  
Initial Time ◽  
Dynamic Learning ◽  
Final Time ◽  
Quantum Neural Network ◽  
Qubit System

We present a dynamic learning paradigm for ``programming'' a general quantum computer. A learning algorithm is used to find the control parameters for a coupled qubit system, such that the system at an initial time evolves to a state in which a given measurement corresponds to the desired operation. This can be thought of as a quantum neural network. We first apply the method to a system of two coupled superconducting quantum interference devices (SQUIDs), and demonstrate learning of both the classical gates XOR and XNOR. Training of the phase produces a gate similar to the CNOT. Striking out for somewhat more interesting territory, we attempt learning of an entanglement witness for a two qubit system. Simulation shows a reasonably successful mapping of the entanglement at the initial time onto the correlation function at the final time for both pure and mixed states. For pure states this mapping requires knowledge of the phase relation between the two parts; however, given that knowledge, this method can be used to measure the entanglement of an otherwise unknown state. The method is easily extended to multiple qubits or to quNits.

A Review of Deep Machine Learning

2016 ◽  
Vol 24 ◽  
pp. 124-136 ◽  
Author(s):  
Ben Bright Benuwa ◽  
Yong Zhao Zhan ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Frank Banaseka Kataka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Algorithm Design ◽  
Prior State ◽  
Art Techniques

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

scholarly journals Majorization arrow in quantum-algorithm design

2002 ◽  
Vol 66 (2) ◽  
Author(s):  
J. I. Latorre ◽  
M. A. Martín-Delgado
Keyword(s):  
Algorithm Design ◽  
Quantum Algorithm

scholarly journals Survey on Clustering High-Dimensional data using Hubness

2020 ◽  
pp. 01-07
Author(s):  
Miss. Archana Chaudahri ◽  
Mr. Nilesh Vani
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
High Dimensional Data ◽  
Algorithm Design ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
K Nearest Neighbor ◽  
Weighted Voting ◽  
Conventional Machine

Most data of interest today in data-mining applications is complex and is usually represented by many different features. Such high-dimensional data is by its very nature often quite difficult to handle by conventional machine-learning algorithms. This is considered to be an aspect of the well known curse of dimensionality. Consequently, high-dimensional data needs to be processed with care, which is why the design of machine-learning algorithms needs to take these factors into account. Furthermore, it was observed that some of the arising high-dimensional properties could in fact be exploited in improving overall algorithm design. One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs. A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion.

scholarly journals How a Clebsch-Gordan transform helps to solve the Heisenberg hidden subgroup problem

2008 ◽  
Vol 8 (5) ◽  
pp. 438-487
Author(s):  
D. Bacon
Keyword(s):  
Finite Groups ◽  
Algorithm Design ◽  
Quantum Algorithm ◽  
Query Complexity ◽  
Quantum Computers ◽  
Hidden Subgroup Problem ◽  
Unitary Transform ◽  
Subgroup Problem ◽  
Good Measurement ◽  
Optimal Measurements

It has recently been shown that quantum computers can efficiently solve the Heisenberg hidden subgroup problem, a problem whose classical query complexity is exponential. This quantum algorithm was discovered within the framework of using pretty good measurements for obtaining optimal measurements in the hidden subgroup problem. Here we show how to solve the Heisenberg hidden subgroup problem using arguments based instead on the symmetry of certain hidden subgroup states. The symmetry we consider leads naturally to a unitary transform known as the Clebsch-Gordan transform over the Heisenberg group. This gives a new representation theoretic explanation for the pretty good measurement derived algorithm for efficiently solving the Heisenberg hidden subgroup problem and provides evidence that Clebsch-Gordan transforms over finite groups are a new primitive in quantum algorithm design.

scholarly journals Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 341
Author(s):  
Xiu-Zhe Luo ◽  
Jin-Guo Liu ◽  
Pan Zhang ◽  
Lei Wang
Keyword(s):  
Automatic Differentiation ◽  
State Of The Art ◽  
Algorithm Design ◽  
Quantum Algorithm ◽  
Quantum Circuits ◽  
Gpu Acceleration ◽  
Intermediate Representation ◽  
Reversible Computing ◽  
Open Source Framework ◽  
Near Term

We introduce Yao, an extensible, efficient open-source framework for quantum algorithm design. Yao features generic and differentiable programming of quantum circuits. It achieves state-of-the-art performance in simulating small to intermediate-sized quantum circuits that are relevant to near-term applications. We introduce the design principles and critical techniques behind Yao. These include the quantum block intermediate representation of quantum circuits, a builtin automatic differentiation engine optimized for reversible computing, and batched quantum registers with GPU acceleration. The extensibility and efficiency of Yao help boost innovation in quantum algorithm design.

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