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Quantum Information and Computation

Grover-based ashenhurst-curtis decomposition using quantum language quipper

LiYiwei, TsaiEdison, PerkowskiMarek, SongXiaoyu

Published Feb 1, 2019• 0 citations
Publisher

Abstract

Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach ba...
DOI
10.5555/3370239.3370243

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Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach based on Grover’s search algorithm for a generalized Ashenhurst-Curtis decomposition. The method models the decomposition problem as a search problem and constructs the oracle circuit based on the set-theoretic partition algebra. A hybrid quantum-based algorithm takes advantage of the quadratic speedup achieved by Grover’s search algorithm with quantum oracles for finding the minimum-cost decomposition. The method is implemented and simulated in the quantum programming language Quipper. This work constitutes the first attempt to apply quantum computing to functional decomposition.
Quipper is a recently released quantum programming language. In this report, we explore Quipper's programming framework by implementing the Deutsch's, Deutsch-Jozsa's, Simon's, Grover's, and Shor's factoring algorithms. It will help new quantum programmers in an instructive manner. We choose Quipper especially for its usability and scalability though it's an ongoing development project. We have also provided introductory concepts of Quipper and prerequisite backgrounds of the algorithms for readers' convenience. We also have written codes for oracles (black boxes or functions) for individual algorithms and tested some of them using the Quipper simulator to prove correctness and introduce the readers with the functionality. As Quipper 0.5 does not include more than \ensuremath{4 \times 4} matrix constructors for Unitary operators, we have also implemented \ensuremath{8 \times 8} and \ensuremath{16 \times 16} matrix constructors.
Quantum machine learning has emerged as a promising field, offering the potential to revolutionize areas such as pattern recognition, data analysis, and optimization. However, current quantum algorithms are often constrained to local computations, hindering their ability to fully exploit the power of quantum mechanics. This paper explores a novel approach to quantum machine learning, utilizing wave function decomposition to facilitate non-local data representation and significantly accelerate computations. We propose a quantum algorithm that decomposes data into entangled states, enabling computations across distances without the need for data transmission. This approach tackles the limitations of current quantum machine learning by introducing a fundamental shift towards distributed computation. The core mechanism centers around the creation of entangled states, allowing for parallel processing across multiple qubits. This paper details the algorithm's design, mathematical underpinnings, and preliminary experimental results demonstrating its potential for accelerating machine learning tasks. We aim to provide a foundation for future research exploring the application of wave function decomposition in quantum machine learning.
Emotion Recognition plays an important role in recognizing human behavior. Human behavior may be represented in text, speech, and facial expressions. In traditional form, natural language processing known as NLP is used to extract features to evaluate emotions. With the evolution of Quantum Computing, Quantum Machine learning often called QML is one of the research areas used to evaluate emotions. This paper is designed to compare traditional ML models with QML models to analyze the sentiments and emotions of multimodal data. Here, NLP techniques are used for preprocessing. A hybrid model is built by combining NLP and QML. The preprocessed data are transmitted into quantum circuits for computation, and its results are compared with conventional machine learning models. This evaluation identifies QML as a promising future as it is useful for large and complex data sets with high dimensions. This study highlights the potential of QML in NLP and outlines the challenges such as encoding and hardware limitations.