Introduction: The frontier of innovation is increasingly shaped by the convergence of transformative technologies. Among these, Generative AI and Quantum Computing stand out, offering untapped potential when harmonized. This proposal delves deep into the fusion of these technologies to sculpt Digital Twins - dynamic, real-time virtual replicas with heightened predictive prowess. Understanding the Technologies: Generative AI: This refers to sophisticated algorithms with the ability to autonomously generate new, high-quality content. Think of it as the artistic hand of AI, crafting images, texts, and other forms of data. Quantum Computing: Beyond the realm of classical bits lies the quantum bit. Quantum Computing uses these bits to enact computations at speeds that classical computers can only dream of. This velocity is not just about speed but also the proficiency in managing immense datasets and intricate computations. Why Quantum Computing is Integral to AI's Evolution: Classic AI models, despite their strengths, hit a wall when faced with monumental data and complex optimization. Quantum Machine Learning (QML) bridges this gap. QML, a vibrant sub-discipline within AI, channels quantum algorithms to supercharge each phase of machine learning. The marriage of Generative AI and Quantum Computing births an ecosystem where training elaborate models not only becomes swift but also diversifies the realm of content generation. Applications within the BFSI Sector: BFSI - Banking, Financial Services, and Insurance - is a realm characterized by data-heavy processes. Imagine a world where: Risk Assessment: Real-time insights from Digital Twins predict market fluctuations with unparalleled precision. Fraud Detection: By harnessing vast datasets, systems instantly flag and mitigate unusual transactions. Portfolio Optimization: Investors access optimized portfolios, benefiting from quantum-enhanced computations that account for countless variables. Algorithmic Trading: The trading world becomes more fluid and responsive, with algorithms making informed decisions in near real-time. Roadmap to Implementation: To bring this vision to life, our interdisciplinary team is poised to: Design models bespoke to BFSI needs. Engage with industry stakeholders, forging strong partnerships. Pilot and refine the approach, ensuring tangible, real-world validation.

1. Digital Twins: The
Intersectionof
Generative AI and
Quantum Computing
Exploring New Frontiersin
Technology
Attemptedby
DrAnshulSaxena

2. Agenda
• Introduction to GenerativeAI
• Introduction to Quantum Computing
• The Need for Quantum Computing in AI
• Quantum Machine Learning
• GenerativeAI on Quantum Computers
• BFSI Applications
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20%
50%

3. Introduction to Generative AI
• Definition of GenerativeAI
• Examples:GenerativeAdversarial Networks (GANs),Variational
Autoencoders, Natural LanguageProcessing (e.g.,Chatbots, Text
Generators)
• Applications: Image and Music Generation, Data Augmentation,
PersonalizedContent

4. How real is Generative AI

5. Introduction to Quantum Computing
• Definition of Quantum Computing
• Qubits, Superposition, and Entanglement
• Prominent Quantum Computing Models: Quantum Circuit Model, Quantum
Annealing
• Current State of Quantum Computing and companies involved (IBM, Google,
Microsoft, etc.)

6. Quantum Annealers
vs Quantum Computers

7. Challenges in Classical Computing
• Computation Time
• Energy Efficiency
The potential of Quantum Computing
• Handling Complex Problems
• Speed
The Need for Quantum
Computingin AI

8. Quantum Machine
Learning
• Definitionof QuantumMachineLearning (QML)
• Benefitsof QML
• How QuantumComputers Can Optimize Machine
Learning Algorithms

9. CanI makeit morereal

10. • The Marriage of GenerativeAI and Quantum
Computing
• Potential Speedupin TrainingGenerativeModels
• QuantumGenerativeModels
Generative AI on
Quantum Computers

12. BFSI APPLICATIONS

13. Data Sources

14. GAN Architecture

15. Feature Engineering

16. Feature Engineering

17. LLM’s

18. QuantumMachine
Learning

19. BFSI Applications

20. BFSI Applications

21. Parametersof
Differentiation
• ComputationalEfficiency: Training Data
• Noise andError Tolerance
• Complexity in Implementation
• Interpretability andRegulatoryCompliance
• Availabilityof Tools andLibraries
• Future Potential

22. • The potential of combining Generative AI
with Quantum Computing
• Call to Action: Encouraging Further
Research and Exploration