Machine Learning and Deep Learning in the Quantum Era 2024: A Market Forecast and Technology Assessment

E.1 Factors Driving the Quantum Machine Learning Market
E.2 Opportunities in Algorithms and Software for QML
E.2.1 Translating ML into QML: The First Phase of QML
E.2.2 New Algorithms and Products:  The Second Phase of QML
E.3 Thoughts on Deep Learning
E.4 Advantages of QML
E.4.1 Improved Optimization and Generalization
E.4.2 QML and Quantum Advantage
E.5 The Disadvantages of QML
E.5.1 High Cost of QCs
E.5.2 Early Stage of Technology
E.5.3 The Workforce Problem
E.6 QML Roadmap and Forecasts

1.1 Objective of this Report
1.2 QML: Possible Advantages
1.2.1 Training Advantages and Opportunities
1.2.2 Quantum Advantage and ML
1.2.3 Improved Accuracy
1.3 QML: Possible Disadvantages
1.3.1 Training Challenges
1.3.2 Uncertainty Regarding Quantum Advantage
1.3.3 Quantum Memory Issues
1.3.4 Comparisons of the Prospects and Challenges of QML at the Present Time
1.4 Plan of this Report
1.5 Information Sources
1.6 Forecasting Methodology

2.1 Machine Learning and its Emergence
2.2 Types of Machine Learning
2.3 Quantum Deep Learning and Quantum Neural Networks
2.3.1 Quantum Deep Learning (a.k.a. Deep Quantum Learning)
2.3.2 Training Quantum Neural Networks
2.3.3 Possible Applications for Quantum Neural Networks
2.3.4 Types of Neural Networks
2.3.5 Quantum Generative Adversarial Networks
2.4 The Rise of Quantum Backpropagation
2.5 Transformers in QML
2.6 Perceptrons in QDL
2.7 Some Notes on ML and Datasets
2.8 Quantum Algorithms: Development and Opportunities
2.8.1 Quantum Encoding
2.8.2 Example of other QML Algorithms
2.8.3 Hybrid Quantum/Classical ML and the Path to True QML
2.9 Handling Larger Data Sets: Quantum Principal Component Analysis
2.9.1 Dimensionality Reduction: Quantum Principal Component Analysis
2.10 Uses of Grover’s Algorithm
2.11 Improved Optimization Techniques
2.12 QML-over-the-Cloud and QML-as-a-Service
2.13 Security and Privacy in QML
2.13.1 Growing QML Vulnerabilities During the Training and Inference Phases
2.13.2 Security on QML Clouds and QML-as-a-Service
2.13.3 Security on QML Architecture
2.14 QML Software Companies
2.14.1 Dassault/Abaqus (United States)
2.14.2 GenMat (United States)
2.14.3 Google (United States)
2.14.4 Microsoft (United States)
2.14.5 OTI Lumionics
2.14.6 PennyLane/Xanadu (Canada)
2.14.7 ProteinQure (Canada)
2.14.8 1Qbit and Good Chemistry (Canada)
2.14.9 QC Ware (United States)
2.14.10 QpiAI (India)
2.14.11 Quantistry (Germany)

3.1 Quantum Annealing
3.1.1 A Note on Boltzman Machines
3.1.2 D-Wave (Canada)
3.2 NISQ Computers and QML
3.2.1 Amazon/AWS (United States)
3.2.2 Atom Computing
3.2.3 Google AI (United States)
3.2.4 IBM (United States)
3.2.5 IonQ (United States)
3.2.6 Nordic Quantum Computing Group (Norway)
3.2.7 ORCA Computing (UK)
3.2.8 Oxford Quantum Circuits (United Kingdom)
3.2.9 Pasqal (France)
3.2.10 planqc (Germany)
3.2.11 QuEra (United States)
3.2.12 Quantinuum (United States)
3.2.13 Rigetti (United States)
3.2.14 Terra Quantum (Switzerland)
3.3 QML beyond NISQ
3.4 Fabricating and Optimizing Quantum Hardware Using QML
3.4.1 Mind Foundry (United Kingdom)
3.4.2 QuantrolOx (UK/Finland)
3.5 A Note on Machine Learning and QRNGs

4.1 Introduction to QML Opportunities
4.2 Financial and Banking Applications for QML
4.2.1 Adaptive Finance (Canada)
4.2.2 Qkrishi (India)
4.3 Healthcare and Life Sciences
4.3.1 Impact of Sensors as a Source of QML-based Diagnostic Data
4.3.2 QML and Personalized Medicine
4.3.3 Pharma and QML
4.3.4 Kuano (Lithuania)
4.3.5 QunaSys (Japan)
4.3.6 MentenAI (Canada)
4.4 Manufacturing Sector Applications for QML
4.5 Other Applications for QML

5.1 Background to Forecasts
5.1.1 Reasons to Doubt QML
5.2 Forecast of QML as Technology
5.3 Forecast of QML by Application

Exhibit E-1: Ten-year Revenues from Quantum Machine Learning and Quantum Deep Learning ($ Millions)
Exhibit 1-1: Variations on a QML Theme: The Six Segments of the Quantum Machine Language Market
Exhibit 1-2: Pros and Cons of QML
Exhibit 2-1: The Relationship Between AI, Machine Learning, Deep Learning and Quantum Computing
Exhibit 2-2: Types of ML Learning
Exhibit 2-3: Selected Neural Network Type/Algorithms
Exhibit 2-4: ML Transformer Applications
Exhibit 2-5: Characteristics of ML Data by Source
Exhibit 2-6: Selected QML Encoding Schemes
Exhibit 2-7: Other QML Algorithms of Importance
Exhibit 4-1: Selected Applications for QML in Banking and Financial Services
Exhibit 4-2: Other Potential Applications of QML
Exhibit 5-1:  Ten-year Revenues from Quantum Machine Learning and Quantum
Deep Learning ($ Millions)
Exhibit 5-2:  Ten-year Revenues - QML/ QDL by Application ($ Millions)