量子機械学習（QML）の世界市場（2026年～2040年）

量子機械学習（QML）は、量子力学のユニークな特性である重ね合わせ、エンタングルメント、量子干渉を利用し、古典的なコンピューターよりも指数関数的に高速に機械学習問題を解決できる可能性があります。量子機械学習は、量子アルゴリズムが量子重ね合わせによって膨大なデータセットを同時に処理し、複数の計算を並行して行うことを可能にする、計算知能のパラダイムシフトを意味します。0か1かの決定的な状態で存在する古典的なビットとは異なり、量子ビット（qubit）は重ね合わせ状態で存在することができるため、量子コンピューターは複数の解の経路を同時に探索することができます。この量子の優位性は、最適化問題、パターン認識、機械学習用途の中核をなす複雑なデータ分析タスクにおいて特に顕著となります。

この分野には、量子プロセッサーを用いて古典的アルゴリズムを高速化する量子強化機械学習や、量子力学的特性を活用した全く新しいアルゴリズムである量子ネイティブ機械学習など、複数の重要なアプローチが含まれます。量子ニューラルネットワーク、量子サポートベクトルマシン、量子強化学習は、AIシステムの学習方法や意思決定方法を根本的に変える可能性のある新たな手法です。

現在の実装では、量子プロセッサーが特定の計算タスクを処理し、古典的コンピューターがデータの前処理、後処理、システム制御を管理する、量子古典ハイブリッドシステムが中心となっています。このアプローチは、ノイズ、デコヒーレンス、量子ビット数の制限といった現在の量子ハードウェアの制限を緩和しながら、両方のパラダイムの長所を最大限に生かすものです。

市場の将来性は、量子機械学習が大きな利点をもたらす可能性のある、数多くの高価値の用途に及んでいます。金融機関では、ポートフォリオ最適化、リスク分析、不正検知に用いる量子アルゴリズムを研究しており、複数の市場シナリオを同時に処理する能力により、優れた投資戦略が生まれる可能性があります。医療や製薬企業では、量子コンピューターが分子間の相互作用をかつてない精度でシミュレートできる可能性があることから、量子を利用した創薬、タンパク質フォールディング予測、個別化医療への応用が検討されています。

製造部門では、サプライチェーン管理、品質管理、予知保全への量子最適化が評価されており、サイバーセキュリティ用途では、耐量子暗号技術や先進の脅威検知システムが応用されています。この技術の将来性は、気候モデリング、交通最適化、科学研究など、従来の計算機では限界がある用途にも広がっています。

当レポートでは、50～1,000量子ビットの量子システムを特徴とする現在のNoisy Intermediate-Scale Quantum（NISQ）時代を検証しています。これらの量子システムは、まだ普遍的な量子の優位性を示すことはできませんが、複雑なQMLアルゴリズムを確実に実行できるフォールトトレラント量子コンピューターへの重要な足がかりとなります。

主な課題は、環境干渉によって量子状態が急速に劣化する量子デコヒーレンス、古典的計算を上回る量子エラー率、量子プログラミングの専門家の不足などです。また、多くの企業にとってハードウェアのコストは依然として高額であるため、クラウドベースアクセスモデルやQaaS（Quantum-as-a-Service）が必要となっています。

競合情勢としては、量子ハードウェアや量子ソフトウェアプラットフォームを開発する大手技術企業、量子コンピューティングに特化した企業、既存製品に量子機能を統合する伝統的な技術企業などがあります。政府投資、学術研究プログラム、ベンチャーキャピタルからの資金提供により、開発スケジュールと商業利用は加速しています。

当レポートでは、世界の量子機械学習（QML）市場について調査分析し、市場規模と予測、アルゴリズムとソフトウェアの情勢、投資と資金調達のエコシステム、主要企業49社のプロファイルなどの情報を提供しています。

目次

第1章 エグゼクティブサマリー

• 量子機械学習市場の促進要因
• QMLのアルゴリズムとソフトウェア
• 機械学習から量子機械学習へ
• QMLのフェーズ
• 利点
• 課題
• QMLのロードマップ

第2章 イントロダクション

• 量子機械学習とは
• 古典的コンピューティングパラダイムと量子コンピューティングパラダイム
• 量子力学の原理
• 機械学習の基礎
• 交差点：量子とMLを組み合わせる理由
• 市場の進化
• この分野の現状
• 用途とユースケース
• 課題と限界
• 技術と性能のロードマップ

第3章 QMLのアルゴリズムとソフトウェア

• 機械学習
• 機械学習のタイプ
• 量子深層学習と量子ニューラルネットワーク
• 量子バックプロパゲーション
• QMLにおけるTransformer
• QDLにおけるPerceptron
• MLデータセット
• 量子符号化
• 量子古典ハイブリッド機械学習と真のQMLへの道
• 最適化手法
• QML-over-the-CloudとQML-as-a-Service
• QMLにおけるセキュリティとプライバシー
• AI、機械学習、深層学習、量子コンピューティング
• トレーニングと推論のフェーズにおけるQMLの脆弱性の増大
• QML CloudとQML-as-a-Serviceのセキュリティ
• 特許情勢
• QMLアーキテクチャのセキュリティ
• 企業

第4章 QMLハードウェアとインフラ

• 概要
• ロードマップ
• コスト
• 量子アニーリング
• NISQコンピューターとQML
• NISQの先のQML
• QMLを用いた量子ハードウェアの製造と最適化
• 機械学習とQRNG

第5章 QMLの市場と用途

• QMLの機会
• 金融・銀行
  • 概要
  • 用途
  • 企業
• 医療・ライフサイエンス
  • 概要
  • 用途
  • センサー
  • 個別化医療
  • 創薬
  • 製薬・QML
  • 企業
• 製造
  • 概要
  • 用途
• その他の用途
• 産業横断的なQMLの恩恵の分析
• 市場規模と成長予測（2026年～2040年）
• 地域市場
  • 北米
  • 欧州
  • アジア太平洋
  • その他の地域
  • 地域の投資と政策枠組み
• QML市場のセグメンテーション
  • 技術タイプ別
  • 応用分野別
• 市場の促進要因と抑制要因
• QML技術準備度の評価
• 市場成長シナリオ

第6章 投資と資金調達

• ベンチャーキャピタルと民間投資の動向
• 政府の資金援助と国家の取り組み
• 企業の研究開発投資

第7章 企業プロファイル（企業47社のプロファイル）

第8章 用語集

第9章 調査手法

第10章 参考文献

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

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