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量子機器學習(QML)的全球市場(2026年～2040年)
市場調查報告書
商品編碼
1734000

量子機器學習(QML)的全球市場(2026年～2040年)

The Global Quantum Machine Learning Market 2026-2040
出版日期: | 出版商: Future Markets, Inc. | 英文 143 Pages, 50 Tables, 21 Figures | 訂單完成後即時交付

價格

量子機器學習 (QML) 利用量子力學的獨特特性——疊加、糾纏和量子乾涉——有望以比傳統電腦更快的速度解決機器學習問題。量子機器學習代表了計算智慧的範式轉變，使量子演算法能夠同時處理大量資料集，並透過量子疊加並行執行多項計算。與存在於 0 或 1 確定狀態的經典位元不同，量子位元（qubit）可以存在於疊加態，從而使量子電腦能夠同時探索多種解決方案路徑。這種量子優勢在最佳化問題、模式識別和複雜資料分析任務中尤其明顯，而這些任務是機器學習應用的核心。

該領域包含幾種關鍵方法，包括量子增強機器學習（使用量子處理器加速傳統演算法）和量子原生機器學習（利用量子力學特性的全新演算法）。量子神經網路、量子支援向量機和量子強化學習是有望從根本上改變人工智慧系統學習和決策方式的新技術。

目前的實作主要圍繞量子-經典混合系統，其中量子處理器處理某些計算任務，而經典電腦則負責資料預處理、後處理和系統控制。這種方法最大限度地發揮了兩種範式的優勢，同時緩解了當前量子硬體的局限性，例如雜訊、退相干和有限的量子位元數。

量子機器學習的市場潛力涵蓋眾多高價值應用，在這些應用中，量子機器學習可以發揮顯著優勢。金融機構正在探索用於投資組合優化、風險分析和詐欺檢測的量子演算法，這些演算法能夠同時處理多種市場場景，從而製定更優的投資策略。醫療保健和製藥公司正在探索藥物研發、蛋白質折疊預測和個人化醫療領域的量子應用，因為量子電腦或許能夠以前所未有的精確度模擬分子相互作用。

製造業正在評估量子最佳化在供應鏈管理、品質控制和預測性維護中的應用，而網路安全應用則包括量子安全加密和進階威脅偵測系統。該技術的潛力也擴展到氣候建模、交通優化、科學研究以及其他傳統計算受限的應用領域。

本報告探討了目前的雜訊中型量子 (NISQ) 時代，其特點是量子系統擁有 50 至 1,000 個量子位元。雖然這些量子系統尚無法證明其普遍的量子優越性，但它們是邁向能夠可靠執行複雜 QML 演算法的容錯量子電腦的重要基石。

主要課題包括量子退相干（由於環境幹擾，量子態會迅速退化）、超過傳統計算的量子誤差率以及量子程式專家的短缺。此外，硬體成本對許多公司來說仍然過高，因此需要基於雲端的存取模型和量子即服務 (QaaS)。

競爭格局包括開發量子硬體和量子軟體平台的領先科技公司、專注於量子運算的公司，以及將量子技術整合到現有產品中的傳統科技公司。政府投資、學術研究計畫​​和創投基金正在加速量子機器學習 (QML) 的開發過程和商業應用。

本報告提供全球量子機器學習(QML)市場相關調查分析，市場規模與預測，演算法和軟體的形勢，投資與資金籌措的生態系統，主要企業49公司的簡介等資訊。

目錄

第1章 摘要整理

  • 量子機器學習市場推動因素
  • QML 演算法與軟體
  • 從機器學習到量子機器學習
  • QML 的各個階段
  • 優點
  • 課題
  • QML的藍圖

第2章 簡介

  • 什麼是量子機器學習？
  • 經典計算範式與量子計算範式
  • 量子力學原理
  • 機器學習基礎
  • 交叉路口：為何要將量子運算和機器學習結合？
  • 市場發展
  • 領域現狀
  • 應用程式和用例
  • 課題與局限性
  • 技術與效能路線圖

第3章 QML的演算法和軟體

  • 機器學習
  • 機器學習的類型
  • 量子深度學習與量子神經網絡
  • 量子反向傳播
  • QML 中的 Transformer
  • QDL 中的感知器
  • 機器學習資料集
  • 量子編碼
  • 混合量子經典機器學習以及通往真正 QML 的道路
  • 最佳化技術
  • 雲端 QML 和 QML 即服務
  • QML 中的安全與隱私
  • 人工智慧、機器學習、深度學習與量子運算
  • 日益增長的QML 在訓練和推理階段的漏洞
  • QML 雲端和 QML 即服務的安全性
  • 專利態勢
  • QML 架構的安全性
  • 企業級

第4章 QML硬體設備和基礎設施

  • 概述
  • 路線圖
  • 成本
  • 量子退火
  • NISQ 計算機和 QML
  • 超越 NISQ 的 QML
  • 使用 QML 製造和優化量子硬體
  • 機器學習和 QRNG

第5章 QML的市場與用途

  • QML的機會
  • 金融·銀行
    • 概要
    • 用途
    • 企業
  • 醫療·生命科學
    • 概要
    • 用途
    • 感測器
    • 個人化醫療
    • 藥物研發
    • 製藥·QML
    • 企業
  • 製造
    • 概要
    • 用途
  • 其他應用
  • QML 在各產業的優勢 Megumi 分析
  • 市場規模及成長預測 (2026-2040)
  • 區域市場
    • 北美
    • 歐洲
    • 亞太地區
    • 其他地區
    • 地區的投資與政策架構
  • QML市場區隔
    • 各技術類型
    • 各應用領域
  • 市場促進因素與阻礙因素
  • QML技術準備度的評估
  • 市場成長情勢

第6章 投資與資金籌措

  • 創業投資與民間投資趨勢
  • 政府的資金援助和國家的配合措施
  • 企業的研究開發投資

第7章 企業簡介(企業47公司的簡介)

第8章 詞彙表

第9章 調查手法

第10章 參考文獻

Quantum Machine Learning (QML) harnesses the unique properties of quantum mechanics-superposition, entanglement, and quantum interference-to potentially solve machine learning problems exponentially faster than classical computers. Quantum Machine Learning represents a paradigm shift in computational intelligence, where quantum algorithms can process vast datasets simultaneously through quantum superposition, enabling multiple calculations to occur in parallel. Unlike classical bits that exist in definitive states of 0 or 1, quantum bits (qubits) can exist in superposition states, allowing quantum computers to explore multiple solution paths simultaneously. This quantum advantage becomes particularly pronounced in optimization problems, pattern recognition, and complex data analysis tasks that form the core of machine learning applications.

The field encompasses several key approaches including quantum-enhanced machine learning, where classical algorithms are accelerated using quantum processors, and quantum-native machine learning, where entirely new algorithms leverage quantum mechanical properties. Quantum neural networks, quantum support vector machines, and quantum reinforcement learning represent emerging methodologies that could fundamentally transform how artificial intelligence systems learn and make decisions.

Current implementations focus on hybrid quantum-classical systems, where quantum processors handle specific computational tasks while classical computers manage data preprocessing, post-processing, and system control. This approach maximizes the strengths of both paradigms while mitigating current quantum hardware limitations such as noise, decoherence, and limited qubit counts.

The market potential spans numerous high-value applications where quantum machine learning could provide significant advantages. Financial institutions are exploring quantum algorithms for portfolio optimization, risk analysis, and fraud detection, where the ability to process multiple market scenarios simultaneously could yield superior investment strategies. Healthcare and pharmaceutical companies are investigating quantum-enhanced drug discovery, protein folding prediction, and personalized medicine applications, where quantum computers could simulate molecular interactions with unprecedented accuracy.

Manufacturing sectors are evaluating quantum optimization for supply chain management, quality control, and predictive maintenance, while cybersecurity applications include quantum-resistant cryptography and advanced threat detection systems. The technology's potential extends to climate modeling, traffic optimization, and scientific research applications where classical computational limitations currently constrain progress.

The report examines the current Noisy Intermediate-Scale Quantum (NISQ) era, characterized by quantum systems with 50-1000 qubits that exhibit significant noise and limited error correction. While these systems cannot yet demonstrate universal quantum advantage, they serve as crucial stepping stones toward fault-tolerant quantum computers capable of running complex QML algorithms reliably.

Key challenges include quantum decoherence, where quantum states deteriorate rapidly due to environmental interference, quantum error rates that currently exceed classical computation, and the scarcity of quantum programming expertise. Hardware costs remain prohibitive for most organizations, necessitating cloud-based access models and quantum-as-a-service offerings.

The competitive landscape includes technology giants developing quantum hardware and software platforms, specialized quantum computing companies, and traditional technology firms integrating quantum capabilities into existing products. Government investments, academic research programs, and venture capital funding are accelerating development timelines and commercial applications.

Report contents include:

  • Detailed market evolution analysis from 2020 through 2040
  • Comprehensive pros and cons assessment of quantum machine learning
  • Technology and performance roadmap with key development milestones
  • Market segmentation by technology type and application sectors
  • Growth projections with multiple scenario analysis
  • Technology readiness assessment across different quantum platforms
  • Algorithm and Software Landscape
    • Machine learning fundamentals and quantum integration approaches
    • Comprehensive analysis of machine learning types and quantum applications
    • Quantum deep learning and quantum neural network architectures
    • Training methodologies for quantum neural networks
    • Applications and use cases for quantum neural networks across industries
    • Neural network types suitable for quantum implementation
    • Quantum generative adversarial networks development and applications
    • Quantum backpropagation techniques and optimization methods
    • Transformers implementation in quantum machine learning systems
    • Perceptrons in quantum deep learning architectures
    • Dataset characteristics and quantum data encoding requirements
    • Quantum encoding schemes and their performance characteristics
    • Hybrid quantum/classical ML development pathways
    • Advanced optimization techniques for quantum machine learning
    • Cloud-based QML services and quantum-as-a-service platforms
    • Security and privacy considerations in quantum machine learning
    • Patent landscape analysis for QML algorithms and implementations
    • Comprehensive profiles of leading QML software companies
  • Hardware Infrastructure Analysis
    • Quantum computing hardware overview and market assessment
    • Hardware development roadmap through 2040
    • Comprehensive cost analysis for quantum computing systems
    • Quantum annealing systems and their ML applications
    • Comparison between quantum annealing and gate-based systems
    • NISQ computers specifications for machine learning applications
    • Error rates and coherence times across different platforms
    • Hardware optimization using quantum machine learning techniques
    • Quantum random number generators for ML applications
    • Leading hardware companies and their technology approaches
  • Application Sector Analysis
    • Comprehensive QML opportunities across multiple industries
    • Financial services and banking applications including risk analysis and optimization
    • Healthcare and life sciences applications for drug discovery and diagnostics
    • Sensor integration for quantum ML-based diagnostic systems
    • Personalized medicine implementation using quantum algorithms
    • Pharmaceutical applications and drug discovery acceleration
    • Manufacturing sector applications for optimization and quality control
    • Additional applications across various industries and use cases
    • Cross-industry benefit analysis and performance comparisons
  • Market Forecasts and Projections
    • Global QML market size projections by year (2026-2040)
    • Regional market growth rates and compound annual growth rate analysis
    • Market segmentation by technology type with revenue projections
    • Application sector segmentation with detailed revenue forecasts
    • Market drivers versus restraints impact analysis
    • Technology readiness assessment matrix across platforms
    • Hardware versus software revenue split projections
    • Market penetration rates by industry sector
    • Technology adoption milestones and timeline analysis
    • Market growth scenarios including conservative, base, and optimistic projections
    • Technology maturity curve analysis and commercial viability assessment
  • Investment and Funding Ecosystem
    • Venture capital investment trends in QML companies
    • Government funding programs and national quantum initiatives
    • Corporate R&D spending patterns and investment strategies
    • Investment trends segmented by technology focus areas
    • Public-private partnership models and collaboration frameworks
  • Company Profiles and Competitive Analysis
    • Comprehensive profiles of 49 leading companies in the QML ecosystem. Companies profiled include AbaQus, Adaptive Finance, Aliro Quantum, Amazon/AWS, Atom Computing, Baidu Inc., BlueQubit Inc., Cambridge Quantum Computing (CQC), D-Wave, GenMat, Google Quantum AI, IBM, IonQ, Kuano, MentenAI, MicroAlgo, Microsoft, Mind Foundry, Mphasis, Nordic Quantum Computing Group, ORCA Computing, Origin Quantum Computing Technology, OTI Lumionics, Oxford Quantum Circuits, Pasqal, PennyLane/Xanadu, planqc GmbH, Polaris Quantum Biotech (POLARISqb), ProteinQure, and more....

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

  • 1.1. Quantum Machine Learning Market Drivers
  • 1.2. Algorithms and Software for QML
  • 1.3. Machine Learning to Quantum Machine Learning
  • 1.4. QML Phases
    • 1.4.1. The First Phase of QML
    • 1.4.2. The Second Phase of QML
  • 1.5. Advantages
    • 1.5.1. Improved Optimization and Generalization
    • 1.5.2. Quantum Advantage
    • 1.5.3. Training Advantages and Opportunities
    • 1.5.4. Quantum Advantage and ML
    • 1.5.5. Improved Accuracy
  • 1.6. Challenges
    • 1.6.1. Costs
    • 1.6.2. Nascent Technology
    • 1.6.3. Training
    • 1.6.4. Quantum Memory Issues
  • 1.7. QML Roadmap

2. INTRODUCTION

  • 2.1. What is Quantum Machine Learning?
  • 2.2. Classical vs. Quantum Computing Paradigms
  • 2.3. Quantum Mechanical Principles
  • 2.4. Machine Learning Fundamentals
  • 2.5. The Intersection: Why Combine Quantum and ML?
  • 2.6. Market evolution
  • 2.7. Current State of the Field
  • 2.8. Applications and Use Cases
  • 2.9. Challenges and Limitations
  • 2.10. Technology and Performance Roadmap

3. QML ALGORITHMS AND SOFTWARE

  • 3.1. Machine Learning
  • 3.2. Types of Machine Learning
  • 3.3. Quantum Deep Learning and Quantum Neural Networks
    • 3.3.1. Quantum Deep Learning
    • 3.3.2. Training Quantum Neural Networks
    • 3.3.3. Applications for Quantum Neural Networks
    • 3.3.4. Types of Neural Networks
    • 3.3.5. Quantum Generative Adversarial Networks
  • 3.4. Quantum Backpropagation
  • 3.5. Transformers in QML
  • 3.6. Perceptrons in QDL
  • 3.7. ML Datasets
  • 3.8. Quantum Encoding
  • 3.9. Hybrid Quantum/Classical ML and the Path to True QML
    • 3.9.1. Quantum Principal Component Analysis
      • 3.9.1.1. Handling Larger Data Sets
      • 3.9.1.2. Dimensionality Reduction
      • 3.9.1.3. Uses of Grover's Algorithm
  • 3.10. Optimization Techniques
  • 3.11. QML-over-the-Cloud and QML-as-a-Service
  • 3.12. Security and Privacy in QML
  • 3.13. AI, Machine Learning, Deep Learning and Quantum Computing
  • 3.14. Growing QML Vulnerabilities During the Training and Inference Phases
  • 3.15. Security on QML Clouds and QML-as-a-Service
  • 3.16. Patent Landscape
    • 3.16.1. Quantum Machine Learning Patents by Type (2020-2025)
    • 3.16.2. QML Algorithms
  • 3.17. Security on QML Architecture
  • 3.18. Companies

4. QML HARDWARE AND INFRASTRUCTURE

  • 4.1. Overview
  • 4.2. Roadmap
  • 4.3. Costs
  • 4.4. Quantum Annealing
    • 4.4.1. Quantum Annealing vs. Gate-based Systems
    • 4.4.2. Companies
  • 4.5. NISQ Computers and QML
    • 4.5.1. NISQ System Specifications for QML
    • 4.5.2. Companies
  • 4.6. QML beyond NISQ
  • 4.7. Fabricating and Optimizing Quantum Hardware Using QML
  • 4.8. Machine Learning and QRNGs

5. QML MARKETS AND APPLICATIONS

  • 5.1. QML Opportunities
  • 5.2. Finance and Banking
    • 5.2.1. Overview
    • 5.2.2. Applications
    • 5.2.3. Companies
  • 5.3. Healthcare and Life Sciences
    • 5.3.1. Overview
    • 5.3.2. Applications
    • 5.3.3. Sensors
    • 5.3.4. Personalized Medicine
    • 5.3.5. Drug Discovery
    • 5.3.6. Pharma and QML
    • 5.3.7. Companies
  • 5.4. Manufacturing
    • 5.4.1. Overview
    • 5.4.2. Applications
  • 5.5. Other Applications
  • 5.6. Cross-Industry QML Benefit Analysis
  • 5.7. Market Size and Growth Projections (2026-2040)
  • 5.8. Regional Market
    • 5.8.1. North America
    • 5.8.2. Europe
    • 5.8.3. Asia-Pacific
    • 5.8.4. Rest of World
    • 5.8.5. Regional Investment and Policy Framework
  • 5.9. QML Market Segmentation
    • 5.9.1. By Technology Type
    • 5.9.2. By Application Sector
  • 5.10. Market Drivers vs. Restraints
  • 5.11. QML Technology Readiness Assessment
  • 5.12. Market Growth Scenarios

6. INVESTMENT AND FUNDING

  • 6.1. Venture Capital and Private Investment Trends
  • 6.2. Government Funding and National Initiatives
  • 6.3. Corporate R&D Investment

7. COMPANY PROFILES (47 company profiles)

8. GLOSSARY OF TERMS

9. RESEARCH METHODOLOGY

10. REFERENCES

List of Tables

  • Table 1. The Six Segments of the Quantum Machine Language Market
  • Table 2. Quantum Machine Learning Market Drivers
  • Table 3. Opportunities in Algorithms and Software for QML
  • Table 4. Advantages of QML
  • Table 5. QML Challenges
  • Table 6. Comparison of the Prospects and Challenges of QML
  • Table 7. QML Pros and Cons
  • Table 8. Classical ML vs. Quantum ML Performance Comparison
  • Table 9. Types of Machine Learning
  • Table 10. QML Algorithm Classification Matrix
  • Table 11. Quantum Neural Network Architectures Comparison
  • Table 12. Training Time Comparison: Classical vs. Quantum Networks
  • Table 13. Applications for Quantum Neural Networks
  • Table 14. Types of Neural Networks
  • Table 15. Quantum Generative Adversarial Networks
  • Table 16. QML Software Platform Feature Comparison
  • Table 17. ML Transformer Applications
  • Table 18. Cloud-based QML Service Providers Analysis
  • Table 19. Characteristics of ML Data by Source
  • Table 20. QML Encoding Schemes
  • Table 21. QML Development Frameworks Comparison
  • Table 22. QML Security Vulnerability Assessment
  • Table 23. Quantum Machine Learning Patents by Type (2020-2025)
  • Table 24. Patent Landscape in QML Algorithms (2020-2025)
  • Table 25. QML Software Companies
  • Table 26. Quantum Computing Hardware Cost Analysis
  • Table 27. Cloud Access Pricing Models for Quantum Hardware
  • Table 28. Quantum Hardware Performance Metrics Trends
  • Table 29. Quantum Hardware Platform Comparison Matrix
  • Table 30. Quantum Annealing vs. Gate-based Systems for ML
  • Table 31. Companies in Quantum Annealing
  • Table 32. NISQ System Specifications for QML
  • Table 33. Companies in NISQ Computers and QML
  • Table 34. Error Rates and Coherence Times by Platform
  • Table 35. Applications for QML in Banking and Financial Services
  • Table 36. Companies in QML for Banking and Financial Services
  • Table 37. Healthcare and Life Science QML Applications
  • Table 38. Drug Discovery QML vs. Classical ML Performance
  • Table 39. Companies in QML for Healthcare and Life Sciences
  • Table 40. Manufacturing QML Use Cases and Benefits
  • Table 41. Other Potential Applications of QML
  • Table 42. Cross-Industry QML Benefit Analysis
  • Table 44. Revenues from Quantum Machine Learning and Quantum Deep Learning ($ Millions) 2026-2040
  • Table 45. Revenue Projections by Geographic Region
  • Table 46. QML Market Segmentation by Technology Type (2026-2040)-Millions USD
  • Table 47. QML Market Segmentation by Application Sector (2026-2040)-Millions USD
  • Table 48. Market Drivers vs. Restraints Impact Analysis
  • Table 49. QML Technology Readiness Assessment Matrix
  • Table 50. VC Investment in QML Companies (2020-2025)
  • Table 51. Government Funding Programs by Country
  • Table 52. Extensive Glossary of Quantum Machine Learning Terms

List of Figures

  • Figure 1. Machine Learning and Quantum Machine Learning
  • Figure 2. QML Roadmap
  • Figure 3. QML Market Evolution Timeline (2020-2040)
  • Figure 4. Technology and Performance Roadmap
  • Figure 5. QML Hardware Roadmap
  • Figure 6. Financial Services QML Adoption Timeline
  • Figure 7. Manufacturing Sector QML Implementation
  • Figure 8. Global QML Market Size by Year (2026-2040) - Millions USD
  • Figure 9. QML Market Segmentation by Technology Type (2026-2040)-Millions USD
  • Figure 10. QML Market Segmentation by Application Sector (2026-2040)-Millions USD
  • Figure 12. Market Penetration Rates by Industry
  • Figure 13. Technology Adoption Milestones Timeline
  • Figure 14. Market Growth Scenarios (Conservative, Base, Optimistic)
  • Figure 15. IonQ's ion trap
  • Figure 16. IonQ product portfolio