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量子AIの世界市場（～2035年）：コンポーネントタイプ別、展開タイプ別、用途タイプ別、エンドユーザー別、企業タイプ別、地域別、産業動向、予測

Quantum AI Market, Till 2035: Distribution by Type of Component, Type of Deployment, Type of Application, End-User, Type of Enterprise and Geographical Regions: Industry Trends and Global Forecasts

出版日
ページ情報
英文 178 Pages
納期
2～10営業日
カスタマイズ可能
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=149.25円
量子AIの世界市場（～2035年）：コンポーネントタイプ別、展開タイプ別、用途タイプ別、エンドユーザー別、企業タイプ別、地域別、産業動向、予測
出版日: 2025年09月03日
発行: Roots Analysis
ページ情報: 英文 178 Pages
納期: 2～10営業日
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概要

世界の量子AIの市場規模は、現在の2億8,000万米ドルから2035年までに77億9,600万米ドルに達すると予測され、2035年までの予測期間にCAGRで35.29%の成長が見込まれます。

Quantum AI Market-IMG1

量子AI市場：成長と動向

現在、AIのユーザー数は2020年から2倍超に増加し、世界全体で約3億人に達しています。これは量子コンピューティングとAIの革命的な融合を意味します。量子AIは従来のコンピューティングでは効率的な解決に苦労していた複雑な問題に取り組むことで、多くの部門を変革する可能性を秘めていることに注目することが重要です。量子AIの大きな利点には、複雑なシステムの最適化、意思決定プロセスの強化、医療部門における創薬の高速化などがあります。

さらに、量子AIは、金融、医療、エネルギー、気候科学などのさまざまな分野における緊急課題に対して、より深い知見とより効果的なソリューションを提供することで、業務ワークフローを変化させています。主要な産業におけるAIの利用が増加しているのは、インターネットへのアクセスが急速に増加し、社会的認知度が高まっているためです。

量子AI部門は、より高い作業効率の達成を目指したイノベーションとデジタルトランスフォーメーションへの世界的な移行における不可欠な要素として台頭しています。自然言語処理と機械学習は、電力効率を高め、より迅速な対応を可能にすることで、量子AI市場の潜在能力を完全に発揮するのに役立っています。

さらに、量子近似最適化アルゴリズム（QAOA）のような先進のアルゴリズムは、従来のアプローチよりも複雑な最適化問題に効果的に対処できる可能性を示しており、現代の重要な発展として、さまざまな部門における意思決定の向上につながっています。結果として、継続的な技術革新と投資の増加により、量子AI市場は予測期間に大きく成長する見込みです。

当レポートでは、世界の量子AI市場について調査分析し、市場規模の推計と機会の分析、競合情勢、企業プロファイル、メガトレンドなどの情報を提供しています。

目次

セクション1 レポートの概要

第1章 序文

第2章 調査手法

第3章 市場力学

第4章 マクロ経済指標

セクション2 定性的な知見

第5章 エグゼクティブサマリー

第6章 イントロダクション

第7章 規制シナリオ

セクション3 市場の概要

第8章 主要企業の包括的なデータベース

第9章 競合情勢

第10章 ホワイトスペースの分析

第11章 企業の競争力の分析

第12章 量子AI市場におけるスタートアップエコシステム

セクション4 企業プロファイル

第13章 企業プロファイル

  • 章の概要
  • 1QBit
  • Amazon Web Services
  • Cambridge Quantum Computing
  • D-Wave Systems
  • Fujitsu
  • Google
  • Hitachi Digital Services
  • IBM
  • Intel
  • Microsoft
  • PsiQuantum
  • QC Ware
  • Quandela
  • Quantum Machines
  • Rigetti
  • Toshiba
  • Zapata Computing

セクション5 市場動向

第14章 メガトレンドの分析

第15章 アンメットニーズの分析

第16章 特許の分析

第17章 近年の発展

セクション6 市場機会の分析

第18章 世界の量子AI市場

第19章 量子AI市場の機会：コンポーネントタイプ別

第20章 市場機会：展開タイプ別

第21章 市場機会：用途タイプ別

第22章 市場機会：エンドユーザー別

第23章 市場機会：企業形態別

第24章 北米の量子AI市場の機会

第25章 欧州の量子AI市場の機会

第26章 アジアの量子AI市場の機会

第27章 中東・北アフリカ（MENA）の量子AI市場の機会

第28章 ラテンアメリカの量子AI市場の機会

第29章 その他の地域の量子AI市場の機会

第30章 市場集中の分析：主要企業別

第31章 隣接市場の分析

セクション7 戦略的ツール

第32章 勝利の鍵となる戦略

第33章 ポーターのファイブフォース分析

第34章 SWOTの分析

第35章 バリューチェーンの分析

第36章 Rootsの戦略的提言

セクション8 その他の独占的知見

第37章 1次調査からの知見

第38章 レポートの結論

セクション9 付録

目次
Product Code: RAICT300252

Quantum AI Market Overview

As per Roots Analysis, the global quantum AI market size is estimated to grow from USD 280 million in the current year to USD 7,796 million by 2035, at a CAGR of 35.29% during the forecast period, till 2035.

Quantum AI Market - IMG1

The opportunity for quantum AI market has been distributed across the following segments:

Type of Component

  • Hardware
  • Services
  • Software

Type of Deployment

  • Cloud
  • On-Premise

Type of Application

  • Cryptography and Security
  • Machine Learning and Optimization
  • Simulation and Modeling

End User

  • Finance
  • Healthcare
  • Logistics and Supply Chain
  • Others

Type of Enterprise

  • Large
  • Small and Medium Enterprise

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

Quantum AI Market: Growth and Trends

As of now, the number of AI users has more than doubled since 2020, reaching approximately 300 million worldwide. This marks a revolutionary combination of quantum computing and artificial intelligence. It is important to note that quantum AI has the potential to transform numerous sectors by tackling complex issues that conventional computing struggles to resolve efficiently. Some significant benefits of quantum AI include the capability to optimize intricate systems, enhance decision-making processes, and speed up drug discovery in the healthcare sector.

In addition, quantum AI has changed operational workflows by delivering deeper insights and more effective solutions to urgent challenges in various fields such as finance, healthcare, energy, and climate science. The increasing use of AI across key industries is noteworthy due to the rapid increase of internet access and growing public awareness.

The quantum AI sector is emerging as a vital element in the global transition towards innovation and digital transformation aimed at achieving greater work efficiency. Natural language processing and machine learning have been instrumental in realizing the full potential of the quantum AI market by enhancing power efficiency and enabling faster responses.

Moreover, advanced algorithms like the Quantum Approximate Optimization Algorithm (QAOA) have demonstrated potential in addressing complicated optimization issues more effectively than traditional approaches, leading to improved decision-making across various sectors as a significant contemporary development. As a result, with ongoing technological innovations and increasing investments, the quantum AI market is expected to experience significant growth during the forecast period.

Quantum AI Market: Key Segments

Market Share by Type of Component

Based on type of component, the global quantum AI market is segmented into hardware, services and software. According to our estimates, currently, the hardware segment, captures the majority share of the market. The key factors contributing to this dominance include the essential role that quantum hardware development, such as processors and qubits, plays in performing quantum computations. Major tech firms like IBM and Google are making significant investments to enhance the capabilities of quantum processors.

Market Share by Type of Deployment

Based on type of deployment, the quantum AI market is segmented into cloud and on-premise. According to our estimates, currently, the on-premise segment captures the majority of the market. This is largely due to its advantages in control, security, and customization, which are vital for sectors dealing with sensitive information, such as finance, healthcare, and government.

However, the cloud computing segment is expected to grow at a higher CAGR during the forecast period. Key factors contributing to this growth include its scalability, cost-effectiveness, and ease of access. Additionally, by utilizing cloud infrastructure, organizations can tap into advanced quantum computing capabilities without needing to make substantial initial investments in specialized hardware.

Market Share by Type of Application

Based on type of application, the quantum AI market is segmented into quantum cryptography, security, machine learning and optimization and simulation and modeling. According to our estimates, currently, machine learning segment captures the majority share of the market. This growth can be attributed to its essential role in driving progress across numerous industries, such as finance, healthcare, and logistics. In addition, the incorporation of quantum computing significantly improves quantum machine learning algorithms, allowing them to analyze large datasets more effectively and identify complex patterns that traditional computers find challenging to process.

Market Share by End User

Based on end user, the quantum AI market is segmented into finance, healthcare, logistics and supply chain and others. According to our estimates, currently, the finance segment captures the majority share of the market. This can be attributed to its data-heavy nature and the essential requirement for real-time decision-making. Financial institutions produce vast quantities of intricate data that necessitate advanced analytical abilities for activities such as risk management, fraud detection, and portfolio optimization.

However, the healthcare segment is expected to grow at a higher CAGR during the forecast period. This growth can be attributed to the transformative potential of its applications, which improve patient care and streamline medical processes. When combined with AI, quantum computing technology can significantly expedite drug discovery, leading to quicker development of life-saving medications and treatments.

Market Share by Type of Enterprise

Based on type of enterprise, the quantum AI market is segmented into large and small and medium enterprise. According to our estimates, currently, the large-scale firms captures the majority share of the market. This growth can be linked to their ability to invest in cutting-edge quantum AI technologies, leverage significant resources, achieve economies of scale, and foster business expansion.

Market Share by Geographical Regions

Based on geographical regions, the quantum AI market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and the rest of the world. According to our estimates, currently, North America captures the majority share of the market. However, the market in Asia is expected to grow at a higher CAGR during the forecast period, driven by significant investments, government initiatives, and increasing demand for quantum AI in nations like China and India.

Example Players in Quantum AI Market

  • 1QBit
  • Amazon Web Services
  • Cambridge Quantum Computing
  • D-Wave Systems
  • Fujitsu
  • Google
  • Hitachi Digital Services
  • IBM
  • Intel
  • Microsoft
  • PsiQuantum
  • QC Ware
  • Quandela
  • Quantum Machines
  • Rigetti
  • Toshiba
  • Zapata Computing

Quantum AI Market: Research Coverage

The report on the quantum AI market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the quantum AI market, focusing on key market segments, including [A] type of component, [B] type of deployment, [C] type of application, [D] end-user, [E] type of enterprise and [F] geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the quantum AI market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the quantum AI market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] quantum AI portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in quantum AI industry.
  • Patent Analysis: An insightful analysis of patents filed / granted in the quantum AI domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
  • Recent Developments: An overview of the recent developments made in the quantum AI market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the quantum AI market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
  • Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the quantum AI market.

Key Questions Answered in this Report

  • How many companies are currently engaged in quantum AI market?
  • Which are the leading companies in this market?
  • What factors are likely to influence the evolution of this market?
  • What is the current and future market size?
  • What is the CAGR of this market?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
  • The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

Additional Benefits

  • Complimentary Excel Data Packs for all Analytical Modules in the Report
  • 15% Free Content Customization
  • Detailed Report Walkthrough Session with Research Team
  • Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of Quantum AI Market
    • 6.2.1. Type of Component
    • 6.2.2. Type of Deployment
    • 6.2.3. Type of Application
    • 6.2.4. Type of End-User
    • 6.2.5. Type of Enterprise
  • 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

  • 9.1. Chapter Overview
  • 9.2. Quantum AI: Overall Market Landscape
    • 9.2.1. Analysis by Year of Establishment
    • 9.2.2. Analysis by Company Size
    • 9.2.3. Analysis by Location of Headquarters
    • 9.2.4. Analysis by Ownership Structure

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE QUANTUM AI MARKET

  • 12.1. Quantum AI Market: Market Landscape of Startups
    • 12.1.1. Analysis by Year of Establishment
    • 12.1.2. Analysis by Company Size
    • 12.1.3. Analysis by Company Size and Year of Establishment
    • 12.1.4. Analysis by Location of Headquarters
    • 12.1.5. Analysis by Company Size and Location of Headquarters
    • 12.1.6. Analysis by Ownership Structure
  • 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

  • 13.1. Chapter Overview
  • 13.2. 1QBit*
    • 13.2.1. Company Overview
    • 13.2.2. Company Mission
    • 13.2.3. Company Footprint
    • 13.2.4. Management Team
    • 13.2.5. Contact Details
    • 13.2.6. Financial Performance
    • 13.2.7. Operating Business Segments
    • 13.2.8. Service / Product Portfolio (project specific)
    • 13.2.9. MOAT Analysis
    • 13.2.10. Recent Developments and Future Outlook
  • 13.3. Amazon Web Services
  • 13.4. Cambridge Quantum Computing
  • 13.5. D-Wave Systems
  • 13.6. Fujitsu
  • 13.7. Google
  • 13.8. Hitachi Digital Services
  • 13.9. IBM
  • 13.10. Intel
  • 13.11. Microsoft
  • 13.12. PsiQuantum
  • 13.13. QC Ware
  • 13.14. Quandela
  • 13.15. Quantum Machines
  • 13.16. Rigetti
  • 13.17. Toshiba
  • 13.18. Zapata Computing

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. UNMEET NEED ANALYSIS

16. PATENT ANALYSIS

17. RECENT DEVELOPMENTS

  • 17.1. Chapter Overview
  • 17.2. Recent Funding
  • 17.3. Recent Partnerships
  • 17.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

18. GLOBAL QUANTUM AI MARKET

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Trends Disruption Impacting Market
  • 18.4. Demand Side Trends
  • 18.5. Supply Side Trends
  • 18.6. Global Quantum AI Market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 18.7. Multivariate Scenario Analysis
    • 18.7.1. Conservative Scenario
    • 18.7.2. Optimistic Scenario
  • 18.8. Investment Feasibility Index
  • 18.9. Key Market Segmentations

19. QUANTUM AI MARKET OPPORTUNITY BASED ON TYPE OF COMPONENT

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. Quantum AI Market for Hardware: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.7. Quantum AI Market for Services: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.8. Quantum AI Market for Software: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.9. Data Triangulation and Validation
    • 19.9.1. Secondary Sources
    • 19.9.2. Primary Sources
    • 19.9.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON TYPE OF DEPLOYMENT

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. Quantum AI Market for Cloud: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.7. Quantum AI Market for On-Premise: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.8. Data Triangulation and Validation
    • 20.8.1. Secondary Sources
    • 20.8.2. Primary Sources
    • 20.8.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF APPLICATION

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. Quantum AI Market for Cryptography and Security: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.7. Quantum AI Market for Machine Learning and Optimization: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.8. Quantum AI Market for Simulation and Modeling: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.9. Data Triangulation and Validation
    • 21.9.1. Secondary Sources
    • 21.9.2. Primary Sources
    • 21.9.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON END-USER

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. Revenue Shift Analysis
  • 22.4. Market Movement Analysis
  • 22.5. Penetration-Growth (P-G) Matrix
  • 22.6. Quantum AI Market for Finance: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.7. Quantum AI Market for Healthcare: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.8. Quantum AI Market for Logistics and Supply Chain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.9. Quantum AI Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.10. Data Triangulation and Validation
    • 22.10.1. Secondary Sources
    • 22.10.2. Primary Sources
    • 22.10.3. Statistical Modeling

23. MARKET OPPORTUNITIES BASED ON TYPE OF ENTERPRISE

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. Revenue Shift Analysis
  • 23.4. Market Movement Analysis
  • 23.5. Penetration-Growth (P-G) Matrix
  • 23.6. Quantum AI Market for Large: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.7. Quantum AI Market for Small and Medium Enterprise: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.8. Data Triangulation and Validation
    • 23.8.1. Secondary Sources
    • 23.8.2. Primary Sources
    • 23.8.3. Statistical Modeling

24. MARKET OPPORTUNITIES FOR QUANTUM AI IN NORTH AMERICA

  • 24.1. Chapter Overview
  • 24.2. Key Assumptions and Methodology
  • 24.3. Revenue Shift Analysis
  • 24.4. Market Movement Analysis
  • 24.5. Penetration-Growth (P-G) Matrix
  • 24.6. Quantum AI Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.1. Quantum AI Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.2. Quantum AI Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.3. Quantum AI Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.4. Quantum AI Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR QUANTUM AI IN EUROPE

  • 25.1. Chapter Overview
  • 25.2. Key Assumptions and Methodology
  • 25.3. Revenue Shift Analysis
  • 25.4. Market Movement Analysis
  • 25.5. Penetration-Growth (P-G) Matrix
  • 25.6. Quantum AI Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.1. Quantum AI Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.2. Quantum AI Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.3. Quantum AI Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.4. Quantum AI Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.5. Quantum AI Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.6. Quantum AI Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.7. Quantum AI Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.8. Quantum AI Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.9. Quantum AI Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.10. Quantum AI Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.11. Quantum AI Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.12. Quantum AI Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.13. Quantum AI Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.14. Quantum AI Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.15. Quantum AI Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.16. Quantum AI Market in Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR QUANTUM AI IN ASIA

  • 26.1. Chapter Overview
  • 26.2. Key Assumptions and Methodology
  • 26.3. Revenue Shift Analysis
  • 26.4. Market Movement Analysis
  • 26.5. Penetration-Growth (P-G) Matrix
  • 26.6. Quantum AI Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.1. Quantum AI Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.2. Quantum AI Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.3. Quantum AI Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.4. Quantum AI Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.5. Quantum AI Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.6. Quantum AI Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR QUANTUM AI IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 27.1. Chapter Overview
  • 27.2. Key Assumptions and Methodology
  • 27.3. Revenue Shift Analysis
  • 27.4. Market Movement Analysis
  • 27.5. Penetration-Growth (P-G) Matrix
  • 27.6. Quantum AI Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.1. Quantum AI Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
    • 27.6.2. Quantum AI Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.3. Quantum AI Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.4. Quantum AI Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.5. Quantum AI Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.6. Quantum AI Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.7. Quantum AI Market in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.8. Quantum AI Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.7. Data Triangulation and Validation

28. MARKET OPPORTUNITIES FOR QUANTUM AI IN LATIN AMERICA

  • 28.1. Chapter Overview
  • 28.2. Key Assumptions and Methodology
  • 28.3. Revenue Shift Analysis
  • 28.4. Market Movement Analysis
  • 28.5. Penetration-Growth (P-G) Matrix
  • 28.6. Quantum AI Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.1. Quantum AI Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.2. Quantum AI Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.3. Quantum AI Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.4. Quantum AI Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.5. Quantum AI Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.6. Quantum AI Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.7. Data Triangulation and Validation

29. MARKET OPPORTUNITIES FOR QUANTUM AI IN REST OF THE WORLD

  • 29.1. Chapter Overview
  • 29.2. Key Assumptions and Methodology
  • 29.3. Revenue Shift Analysis
  • 29.4. Market Movement Analysis
  • 29.5. Penetration-Growth (P-G) Matrix
  • 29.6. Quantum AI Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.1. Quantum AI Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.2. Quantum AI Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.3. Quantum AI Market in Other Countries
  • 29.7. Data Triangulation and Validation

30. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

  • 30.1. Leading Player 1
  • 30.2. Leading Player 2
  • 30.3. Leading Player 3
  • 30.4. Leading Player 4
  • 30.5. Leading Player 5
  • 30.6. Leading Player 6
  • 30.7. Leading Player 7
  • 30.8. Leading Player 8

31. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

32. KEY WINNING STRATEGIES

33. PORTER'S FIVE FORCES ANALYSIS

34. SWOT ANALYSIS

35. VALUE CHAIN ANALYSIS

36. ROOTS STRATEGIC RECOMMENDATIONS

  • 36.1. Chapter Overview
  • 36.2. Key Business-related Strategies
    • 36.2.1. Research & Development
    • 36.2.2. Product Manufacturing
    • 36.2.3. Commercialization / Go-to-Market
    • 36.2.4. Sales and Marketing
  • 36.3. Key Operations-related Strategies
    • 36.3.1. Risk Management
    • 36.3.2. Workforce
    • 36.3.3. Finance
    • 36.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

37. INSIGHTS FROM PRIMARY RESEARCH

38. REPORT CONCLUSION

SECTION IX: APPENDIX

39. TABULATED DATA

40. LIST OF COMPANIES AND ORGANIZATIONS

41. CUSTOMIZATION OPPORTUNITIES

42. ROOTS SUBSCRIPTION SERVICES

43. AUTHOR DETAILS