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Monetizing Quantum Computing

Nir Kshetri

Published Jan 1, 2024• 7 citations
Publisher

Abstract

Quantum computers demonstrate significant speed advantages over classical computers in specific tasks. This article explores the current quantum computing landscape, encompassing investment, market growth, and the potential value generation across key industries.
DOI
10.1109/mitp.2024.3356111

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It is expected that quantum computers would enable solving various problems that are beyond the capabilities of the most powerful current supercomputers, which are based on classical technologies. In the last three decades, advances in quantum computing stimulated significant interest in this field from industry, investors, media, executives, and general public. However, the understanding of this technology, its current capabilities and its potential impact in these communities is still lacking. Closing this gap requires a complete picture of how to assess quantum computing devices' performance and estimate their potential, a task made harder by the variety of quantum computing models and physical platforms. Here we review the state of the art in quantum computing, promising computational models and the most developed physical platforms. We also discuss potential applications, the requirements posed by these applications and technological pathways towards addressing these requirements. Finally, we summarize and analyze the arguments for the quantum computing market's further exponential growth. The review is written in a simple language without equations, and should be accessible to readers with no advanced background in mathematics and physics.
In the article the authors aim at presenting an overview of the current state of quantum computing, exploring qubits, gates, circuits, error correction, sample algorithms, and potential applications as well as an overview of current and near future applications. The article highlights the rapid growth in quantum computing research and argues that now is the opportune time to pursue practical applications. By delving into the fundamental aspects of quantum computing and examining the challenges and advancements in the field, the article provides a comprehensive understanding of the current landscape and emphasizes the importance of scaling and accelerating the development of quantum technologies to unlock their full potential
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
Abstract This chapter explores the ongoing substantial financial investments required for developing and commercializing quantum computing technology. Given the field’s high uncertainty and long-term potential, investments are crucial for advancing breakthroughs in cryptography, materials science, and complex system simulations. The chapter examines the rise of quantum computing funding from the private equity and venture capital standpoints, highlighting key players like IBM and Google and emerging firms such as Rigetti and IonQ. It provides a detailed analysis of private funding trends, valuations, and geographic distribution, underscoring the sector’s growth despite market volatility and regional concentration challenges. It also focuses on the discrepancies in funding allocation and performance among startups and the significant role of established companies like Microsoft, Google, and IBM in shaping the industry. Looking ahead, the chapter emphasizes the need for innovative financial models to assess investment returns, considering the nascent nature of quantum technology and the scarcity of financial data from private firms.