Good morning everyone. Thank you for being here today. My name is Siva Prakash, and it's a genuine pleasure to speak with you about what I believe is the next great frontier in financial technologies. For over two decades, I have had the privilege of working within the IT landscape, witnessing wave after wave of innovation. In my current role, I see firsthand the immense computational challenges. Point where the limits of classical computings are becoming increasingly apparent just as a new paradigm is downing. Today we are going to explore that paradigm, the integration of quantum computing principles with real-time financial data processing. We will be looking at a future powered by quantum enhanced real-time data shaping and feature stores are future design for ultra low latency demands of modern finance. Understand where we are going. We first need to appreciate where we are. The world of finance today is convergence of four powerful forces. First, the demand from financial application. We are no longer in an era of end of batch processing. Finance demands microsecond level decision, whether in high frequency trading, realtime product preventions, or dynamic risk assessment. This is fueled by data streaming technologies, which act as the global financial system circulatory system, enabling a continuous realtime flow of market data, transactions and emails. To make sense of this data, we rely on feature stores, which centralizes. The critical data ingredients, the features that power our machine learning models consistently and at scale. And now entering this quantum computing, which promises a fundamentally new way to compute, leverage the principle of quantum mechanics for powerful exploration. The reason this conversion is so critical is because we're hitting a ball. We call it the classical limitations. For decades, we have relied on Moore and advanced parallelization with GPUs to keep up the data deluge. But we are now facing insurmountable BO bottlenecks. The sheer volume of data from market feeds news and alternative sources one challenge, but the bigger challenge is complexity. When we analyze financial data sets with hundreds of correlating risk across finding. Pattern across millions of transaction, the computational complexity through exponentially. This is not a linear problem for a classical computer doubling the number of variable cans, square, or even queue the time needed for a solution. At a certain point, a realtime processing becomes practically impossible. We can build bigger data center, but we can't out architect the map. That is where we need a new tool, a new approach, and the approach is quantum. So what makes quantum computing so different? Let's demy and demystify it. Imagine a classical computer bit. It's like a light switch. It can be either on or off. It's binary and definite. A quantum bit or qubit is fundamentally different. Think of it more like a bimmer switch. It can be on off or crucially in a state of superposition, a blend of both on and off simultaneously. This property of superposition is the first key to quantum power because Abit can hold multiple values at once a. Number of possibilities concurrently. While a classical computer checks scenarios one by one, a quantum computer checks them all at the same time. This is what we call quantum parallelism and it's what allows quantum algorithms to dramatically reduce decision latency for time sensitive financial transaction. The second principle is entanglement instance, famously called it spooky actions at a distance. When two qubits are entangled, their fades are linked. If you measure one, you instantly know the state of the other no matter how far apart they are. For finance, this is a revolutionary. It means we can use entangle. Across vast financial data sets without having to manually sift through every data point. These principles allow quantum computers to solve certain types of problems, specifically optimization, simulation, and complex sampling exponentially faster than any classical computer ever. Code. Now it's crucial to understand that we are not talking about replacing our existing data center with quantum computer. The future, at least for the foreseeable feature, is hybrid. We'll use a combination of classical and quantum system each playing its strengths. This slide shows a high level of. What the quantum enhanced streaming architecture looks like. At the bottom, we have our classical infrastructure. This is your high performance compute, your network fabric, your existing databases. This workhorse layer handles the 99% of task. It's already good at data storage, basic processing, and moving data around. Next we have the real time processing stream processing layer. Using technologies like Apache, Flink and Kafka, this is where the real time processing happens. This is a circulatory system we talked about. We're developing quantum aware connectors that know when to route a specific computationally intensive task away from the classical part. This data then feeds our features storage there, built on platform like Feast or a w SageMaker. This layer is quantum optimized, meaning it can store and serve a feature that are either generated by quantum processes or designed to be consumed by one, and at the top, the pinnacle of the architecture are the quantum processor processing unit. These are specialized accelerator. We don't send every transaction to QPU. We send it the hardest problem, the complex calculations and pattern recognition task that would choke a classical system. In this model, the classical system enables a workflow while the QPU acts as a powerful co-processor tackling the in intractable parts of the problem before feeding the answer back into the classical stream. This is the pragmatic path to quantum advantage. Let us look into the use case. One. Quantum powered Trading. Trading strategies. Let's make this tangible. How does this architecture apply to the demanding world of algorithmic trading? The goal in trading is to find a true signal in a sea of noise and to act on it faster than anyone else. Quantum gives us an on both front signal processing market data is incredibly noisy Quantum. Signal processing. Leveraging principle from quantum sensing can filter this noise and identify faint patterns with incredible precision. Often in nanoseconds, this means getting a cleaner, more accurate picture of the, market. Faster pattern recognition. Many trading strategies rely on identifying complex. Multidimensional pattern and market behavior. Quantum mission learning algorithm can perform this pattern recognition up to a hundred times faster than classical methods, allowing to identify and act on opportunities that would otherwise be missed. Simultaneously, scenarios, perhaps most powerfully used powerfully using super position, a quantum algorithm can over thousand potential rating scenarios simultaneously per microsecond. This. Includes modeling market impact, running risk checks, and optimizing execution strategy. All concurrently, it represents a monumental leap from the sequential analysis of a classical system. Next comes the ultra low latency fraud detection. Another critical area is fraud detection. Every microsecond counts. The goal is not just to detect fraud, but to intervene. Before the transaction is finalized and the money is gone, anomaly detection. Sophisticated fraud often involves subtle deviation from normal patterns spread across many accounts. And transaction. Quantum algorithms are uniquely suited to spotting this microscopic pattern deviation with sub microsecond position. The feature extraction. A key part of modern fraud detection is analyzing the relationship. Our quantum optimized feature stores can deliver incredibly complex. Like the correlation between a group of new accounts making similar transaction in picosecond, look at the time it is in picosecond. This is speed at a level, classical system can't match risk assessment and intervention. Quantum parallelism allows us to evaluate millions of risk vector simultaneously for single transaction. An instant highly accurate risk score, which in turn enables ultra low latency response capabilities to neutralize fraudulent transaction before completion. This is the difference between foreign analysis after the fact and a realtime prevention. Finally, let's look at an application focused on the end customer. Personalized. The dream has always been to provide truly individual holistic advice that adapts in real time quantum customer segmentation. Today's segmentation is often based on broad demographic data, quantum clustering algorithm. We can process billions of behavioral data points. Inquiries life events to create hyper personalized client segment. A grouping customer by subtle shared behavior needs. Quantum neural networks two mo model the complex, often financial behavior of individuals. We can use quantum neural network. These models can handle exponentially more parameters than their classical counterparts, allowing for a much richer and more accurate understanding of classical client needs. Dynamic portfolio optimization. This is a classic opt classic optimization problem and a perfect fit for quantum. A quantum system can continuously recalculate optimal asset allocation for millions of clients as market condition and correlations shift in real time. This is not a nightly reval, it's a living adaptive p. Personalized recommendation within microseconds of market events, imagine a client's portfolio being automatically adjusted to mitigate risk. The instant relevant geopolitical. This is the future of financial advice. Now it's essential to be realistic. This incredible power comes with equally incredible engineering challenges. This is not easy. First, the quantum environment requirements are extreme. Qubits are fragile. They must be kept in highly isolated environment at cryogenic temperature, older than deep space, offered below 20 milli to maintain their quantum state, a property called coherence that is significant data, trans translation, moving information from. Latency costs that must be meticulously optimized. Error correction is arguably the biggest hurdle because qubits are so fragile, they're prone to error. We must implement complex, full tolerant quantum error correction code to preserve the integrity of our calculation. And finally, there is immerse integration complexity in architecting. Seamless high performance interfaces between quantum, a processor and classical system. This leads to the critical issue of data quality. Garbage in garbage out is even more true for quantum system. We need advanced quantum noise mitigation filters to eliminate signals interfaced at the quantum level. We need continuous calibration system to maintain perfect data synchronization between the quantum and classical worlds with monitoring protocols that ensure uncompromised data integrity. And we need fault tolerant design using techniques like multilayered circuited, ancy to validate mission critical finance and financial transaction with near perfect reliability. Architecting for the future. The quantum optimized feature store facing these challenges does not mean we stop. It means we architect smarter. This slide shows how we are already re-imagining core components like the feature store. For this hybrid feature. We are optimizing it at every level. Quantum feature of competition, the most complex feature and financial indicators. Like derivatives, pricing and or complex risk metrics are calculate, calculated using quantum algorithms at unprecedented speed feature transport optimization. We use quantum inspired routing algorithm to minimize latency in delivering these features to the models that need them, ensuring critical C strategy C. The optimal placement of features in the cache dynamically adjusting based on realtime usage, pattern on demand feature generation for rarely used, but computationally heavy features. We just in time computation, balancing storage cost with performance need by re-architecting our system with this principle in mind today, we will, the foundation, need to plug in quantum processor as they mature. Quantum feature of financial technology. So what does the roadmap of this quantum feature looks like? Right now? We are in the era of early quantum advantage. Specialized quantum algorithms are being used to enhance every specific financial task, demonstrating a clear alphabet, narrow advantage over classical method. In the next two, three years, we will see hybrid system mature. Integrating quantum, classical platform will become more common capable of handling complete financial workflows for specific use cases like portfolio optimization or fraud analysis. Look out for five or more years, we anticipate the rise of quantum financial network. This will be distributed quantum system, possibly linked by quantum communication channels that begin to transform the global financial infrastructure itself. And in 10 or more years, we can envision the emergence of quantum native finance. These will be entirely new financial markets. Of. Quantum computing with real-time data technologies, not science fiction. It is a next logical step in the evolution of finance. Quantum is not a magic bullet that will replace all classical computing. It is a highly specialized tool for new class of computational problems. The problems of. And complexity that are defining the future of in our industry. The journey of quantum advantage is a marathon, not a sprint, and it is fundamentally a hybrid journey. The financial institution that will lead in the next decade are the ones that start preparing now by architecting for this hybrid reality, investing in the right skill and beginning to identify. The specific high value problems where Quantum can deliver a true transformative advantage. The future of finance will be faster, smarter, and more secure, and it'll be in part quantum. Thank you very much for your time and attention. I would be hap happy to answer any question you may have.