Error-correcting codes and applications to large scale classification systems

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 37-39).In this thesis, we study the performance of distributed output coding (DOC) and error-Correcting output coding (ECOC) as potential methods for expanding the class of tractable machine-learning problems. Using distributed output coding, we were able to scale a neural-network-based algorithm to handle nearly 10,000 output classes. In particular, we built a prototype OCR engine for Devanagari and Korean texts based upon distributed output coding. We found that the resulting classifiers performed better than existing algorithms, while maintaining small size. Error-correction, however, was found to be ineffective at increasing the accuracy of the ensemble. For each language, we also tested the feasibility of automatically finding a good codebook. Unfortunately, the results in this direction were primarily negative.by Jeremy Scott Hurwitz.M.Eng

Quantum Approaches to Data Science and Data Analytics

In this thesis are explored different research directions related to both the use of classical data analysis techniques for the study of quantum systems and the employment of quantum computing to speed up hard Machine Learning task

Notes on Quantum Computation and Information

We discuss fundamentals of quantum computing and information - quantum gates, circuits, algorithms, theorems, error correction, and provide collection of QISKIT programs and exercises for the interested reader.Comment: v2: 86 pages, 97 references. Refined the text, fixed several typos, added some text on continuous variables, and added few solved example problems. v1: 72 pages, 76 references. Suggestions, comments, and corrections are very welcome

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications

Algoritmos cuánticos tolerantes a fallos

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 17-01-2023The framework of this thesis is fault-tolerant quantum algorithms, which can roughly be divided into the following non-disjoint families: a) Grover’s algorithm and quantum walks, b) Shor’s algorithm and hidden subgroup problems, c) quantum simulation algorithms, d) quantum linear algebra, and e) variational quantum algorithms. All of them are covered, to some extent, in this thesis. Grover’s algorithm and quantum walks are described in Chapter 2. We start by highlighting the central role that rotations play in quantum algorithms, explaining Grover’s, why it is optimal, and how it may be extended. Key subroutines explained in this area are amplitude amplification and quantum walks, which will constitute useful parts of other algorithms. In this chapter, we present our Ref. [62], where we explore the heuristic use of quantum Metropolis and quantum walk algorithms for solving anNP-hard problem. This method has been suggested as an avenue to digitally simulate quantum annealing and preparing ground states of many-body Hamiltonians. In the third chapter, in contrast, we turn to the exponential advantages promisedby the Fourier transform in the context of the hidden subgroup problem. However, since this application is restricted to cryptography, we later explore its use in quantum linear algebra problems. Here we explain the development of the original quantum linear solver algorithm, its improvements, and finally the dequantization techniques that would often restrict the quantum advantage to polynomial...El marco conceptual de esta tesis son los algoritmos cuánticos tolerantes a fallos, que pueden dividirse aproximadamente en las siguientes clases no mutuamente excluyentes :a) algoritmo de Grover y paseos cuánticos, b) algoritmo de Shor y problemas de subgrupos ocultos, c) algoritmos de simulación cuántica, d) álgebra lineal cuántica, ye) algoritmos cuánticos variacionales. Todos ellos se tratan, en cierta medida, en esta tesis. El algoritmo de Grover y los paseos cuánticos se explican en el capítulo 2. Comenzamos destacando el papel central que juegan las rotaciones en los algoritmos cuánticos, explicando el de Grover, por qué es óptimo, y cómo puede ser extendido. Las subrutinas clave explicadas en esta área son la amplificación de la amplitud y los paseos cuánticos, que serán partes importantes de otros algoritmos. En este capítulo presentamos nuestra Ref. [62], donde exploramos el uso heurístico de los algoritmos de Metrópolis y paseos cuánticos para resolver problemas NP-difíciles. De hecho, este método ha sido sugerido como una vía para simular digitalmente el método conocido como ‘quantum annealing’,y la preparación de estados fundamentales de Hamiltonianos ‘many-body’.En el tercer capítulo, en cambio, nos centramos en las ventajas exponenciales que promete la transformada de Fourier en el contexto del problema de los subgrupos ocultos. Sin embargo, dado que esta aplicación está restringida a la criptografía, más adelante exploramos su uso en problemas de álgebra lineal cuántica. Aquí explicamos el desarrollo del algoritmo cuántico original para la resolución de sistemas lineales de ecuaciones, sus mejoras, y finalmente las técnicas de ‘descuantización’ que a menudo restringen la ventaja cuántica a polinómica...Fac. de Ciencias FísicasTRUEunpu