Data Science and Innovations for Intelligent Systems

Computational Excellence and Society 5.0

Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh, Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh

• Publisher: CRC Press
• Year: 2021
• ISBN:
  9781000456165
• Edition: 1
• Topic: Ciencia de la computación
• Subtopic: Tratamiento de datos

Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems.

This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era.

Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science.

1 Quantum Computing: Computational Excellence for Society 5.0

Paul R. Griffin¹, Michael Boguslavsky², Junye Huang³, Robert J. Kauffman⁴, and Brian R. Tan⁵

¹School of Information Systems Singapore Management University, Singapore

²Tradeteq, London, United Kingdom

³IBM Quantum, Singapore

⁴Copenhagen Business School Denmark, and School of Information Systems, Singapore Management University, Singapore

⁵B.R.I.T. Management Consulting, Singapore

DOI: 10.1201/9781003132080-1

CONTENTS

1.1 Introduction

1.2 Quantum Computing Fundamentals

1.2.1 Key Concepts

1.2.2 Hardware, Software, Alorithms, and Workflow

1.3 Quantum Computing Needs and Service Industry Applications

1.3.1 Business Needs and Concerns

1.3.2 Decision Problem Framing and Computation

1.3.3 The Range of Business Problem Areas That Can Be Addressed

1.3.4 The Unique Role of Quantum Computing in Financial Services Applications

1.4 Application Framework

1.4.1 Algorithm Design

1.4.2 Software Development

1.4.3 Hardware for Quantum Computing

1.4.4 Integration with Other IT Systems in the Firm

1.5 Case: Implementing a Quantum Neural Network for Credit Risk

1.5.1 Credit Risk Assessment

1.5.2 Algorithm Design for a Quantum Neural Network (QNN)

1.5.3 Software Design for QNN

1.5.4 Hardware for Quantum Credit Scoring

1.5.5 Issues for Moving from a Stand-Alone to an Integrated System

1.6 Conclusion

Acknowledgments

Notes

References

Appendix A: Glossary of Terms

1.1 Introduction

Quantum computing is a key part of building an intelligent systems infrastructure for Society 5.0 and can be used in the future across the main pillars of fintech, healthcare, logistics, and artificial intelligence (AI). Intelligent systems based on data science are machines that are sufficiently advanced to be able to perceive and react to external events. Quantum computers offer various avenues to go beyond systems using classical computers and extend computational excellence beyond its current state. Digital innovation underpins the concept of Society 5.0 for a better future with an inclusive, sustainable, and knowledge-intensive society that uses information computing. A key to realizing this society is to utilize gargantuan volumes of data in real-time in intelligent systems. The sharing of information in Society 4.0 has been insufficient and integration of data problematic, whereas Society 5.0 integrates cyberspace and physical space. For example, in Society 5.0, the huge amount of data from physical Internet of Things (IoT) devices are required to be analyzed, processed, and fed back to robotic devices interacting with people in various forms. In Japan alone, the next 15 years is expected to see a growth in IoT and robotics of US$20 bn and US$70 bn, respectively (JapanGov News, 2019). However, the aim of Society 5.0 is to balance economic development and solutions for social issues to bring about a human-centered society. This chapter shows how quantum computing can be applied to many current challenges and open up new opportunities with innovative ways that align better with human thinking.

Classical computing has brought society great benefits over many years from the abacus 3,300 years ago through to modern computing from Alan Turing in the 20th century – to the latest smartphones we are now familiar with. Computers have enabled products and services that humans cannot provide alone such as increasing productivity, enhancing communication, storing vast amounts of data, sorting, organizing, and searching through information amongst many more. However, many problems still exist such as data security, scalability, manageability, and interoperability. Furthermore, Moore’s Law increases in computing power is now beginning to fail (Loeffler, 2018). Stefan Filipp, a quantum scientist at IBM Research, has stated that to “continue the pace of progress, we need to augment the classical approach with a new platform, one that follows its own set of rules. That is quantum computing” (Singh, 2019). Using the advantages of quantum over classical computing, it is possible to increase computing capacity beyond anything that classical computers can achieve.

Quantum computing was suggested in the 1980s by Manin (1980) and Feynman (1982). In the past few years, it has become a reality and accessible to everyone, with IBM putting the first quantum computer on the cloud in 2016. Now, in 2021, there are dozens of quantum computers online with processing capabilities much better than the first one. While there is little doubt that quantum computers can outperform classical computers for some processes, such as unstructured search problems (Grover, 1996), it is not clear whether and how quantum computing will be advantageous for a particular business need or, indeed, worth the effort to investigate further.

This chapter is aimed at providing a framework to assess the likelihood that quantum computing will be an area that is worthwhile to get involved in for particular business opportunities and challenges. The main differences for quantum computing are superposition and entanglement. Traditional computers use bits of either 0 or 1. In contrast, quantum computers use qubits existing in a state that is best described as the probability of being either 0 or 1. This is called the superposition of states (Nielsen & Chuang, 2010).¹ Qubits also exhibit entanglement, whereby they may be spatially nearby or far apart, may interact with one another at certain times, and yet are not able to be characterized as being independent of one another. The result is that two qubits may work together as if they were one larger qubit. This is fundamentally different from bits that are always kept separate in classical computing. However, current quantum computers are noisy and have an insufficient number of qubits to be able to show provable advantages. And even the widely publicized Google experiment (Arute et al., 2019) is still held to be contentious (Pednault, 2020). Even more contentious are annealer-type quantum computers (Rønnow et al., 2014). These will not be covered in this chapter as a result, and we will focus on gate-type quantum computers instead.

Considering the Society 5.0 issues of data volumes, real-time processing and linking data, we present a framework to assess what business needs may potentially be addressed by quantum computing and how quantum computing is different to classical systems. There are four areas of concern: the data, the processing, the infrastructure, and the environment. (See Figure 1.1.) First, data may be complex, natively quantum or probabilistic, for example, chemical reactions involving quantum particles and financial derivatives pricing including many predictor variables. Second, processing may involve a range of different data analytics approaches, such as simulation, machine learning (ML), optimization, and AI. Third, current quantum computing infrastructure solutions vary in their types and specifications, with the overall marketplace in the throes of rapid technological innovation. Last, the environment may have legal regulations in terms of what can be done, how data can be used, and how physical and human resources can be used while protecting private information.

Figure 1.1 The main areas of concern in the quantum application framework.

The remainder of this chapter is laid out as follows. Section 1.2 reviews the main differences between quantum computing and classical computing. It introduces the concepts of qubits, quantum states, and quantum operations. It also gives overviews of different approaches to quantum hardware, as well as the end-to-end process of running a quantum algorithm. Available software and debugging challenges are also discussed, along with some common quantum algorithms. Section 1.3 looks at current managerial concerns and related business problems that quantum computing can potentially address. Section 1.4 offers a framework showing the areas that need to be addressed when considering whether to build a quantum computing solution. Using this framework, Section 1.5 provides an example of its application for a quantum neural network (QNN) solution for the credit rating of small and medium-size enterprises (SMEs). Section 1.6 looks at the future of quantum computing, covering improvement areas for theoretical development, hardware, and integration with analytics software.

While there is no expectation that quantum computers will outperform current classical models in the near term, exploring the requirements and limitations of current quantum computers can be useful for thinking through how to develop a future system to meet business objectives when quantum computing reaches a suitable level of maturity.

1.2 Quantum Computing Fundamentals

This section reviews the fundamental properties of quantum computers, and the end-to-end workflow from data preparati...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Contents
• Editors
• Contributors
• Chapter 1 Quantum Computing: Computational Excellence for Society 5.0
• Chapter 2 Prediction Models for Accurate Data Analysis: Innovations in Data Science
• Chapter 3 Software Engineering Paradigm for Real-Time Accurate Decision Making for Code Smell Prioritization
• Chapter 4 Evaluating Machine Learning Capabilities for Predicting Joining Behavior of Freshmen Students Enrolled at Institutes of Higher Education: Case Study from a Novel Problem Domain
• Chapter 5 Image Processing for Knowledge Management and Effective Information Extraction for Improved Cervical Cancer Diagnosis
• Chapter 6 Recreating Efficient Framework for Resource-Constrained Environment: HR Analytics and its Trends for Society 5.0
• Chapter 7 Integration of Internet of Things (IoT) in Health Care Industry: An Overview of Benefits, Challenges, and Applications
• Chapter 8 Cloud, Edge, and Fog Computing: Trends and Case Studies
• Chapter 9 A Paradigm Shift for Computational Excellence from Traditional Machine Learning to Modern Deep Learning-Based Image Steganalysis
• Chapter 10 Feature Engineering for Presentation Attack Detection in Face Recognition: A Paradigm Shift from Conventional to Contemporary Data-Driven Approaches
• Chapter 11 Reconfigurable Binary Neural Networks Hardware Accelerator for Accurate Data Analysis in Intelligent Systems
• Chapter 12 Recommender System: Techniques for Better Decision Making for Society 5.0
• Chapter 13 Implementation of Smart Irrigation System Using Intelligent Systems and Machine Learning Approaches
• Chapter 14 Lightweight Cryptography Using a Trust-Based System for Internet of Things (IoT)
• Chapter 15 Innovation in Healthcare for Improved Pneumonia Diagnosis with Gradient-Weighted Class Activation Map Visualization
• Index