Hello everyone. Welcome to an exploration of how quantum computing is fundamentally transforming SaaS. We stand at a purpose of a new era where these interconnected forces are reshaping the technological landscape. Quantum computing capabilities, edge processing advancements, and evolve the multi-tenancy models. In this presentation, we'll explore how these converge technologies are creating unprecedented opportunities for software architects and technology leaders to build the next generation of intelligent. Responsive and highly efficient cloud platforms. So if we take a look at the three pillars of quantum Enhanced SaaS represents a new frontier in cloud services, built on three foundational pillars that combine immense power. Immediate responsiveness and efficient delivery. We take a look at quantum computing, unprecedented computational power. At its core, the SaaS model leverages quantum computers to solve problems far beyond the reach of classical machines. This unlocks hyper intelligent ai. That can. Hidden patterns in massive data sets enables Rafa simulations for complex fields like drug discovery and financial modeling, and delivers advanced optimizations for logistics and supply chains. This power is accessed on demand through the cloud, making revolutionary computational capabilities available as a service. Edge processing, distributed diligence near users. Edge processing brings the insights generated by the quantum computers closer to the end user. By processing data locally, it drastically reduces latency, allowing for real time decision making in applications like autonomous vehicles and industrial I Otic. This ensures the quantum and powered solutions are not only intelligent. But also immediately actionable, while also enhancing data privacy by minimizing new raw data transfer, advanced multi-tenancy, optimized resource sharing. This pillar provides the scalable and secure framework to deliver quantum capabilities to multiple customers. It manages the efficient sharing of scales, quantum processes, ensuring fair allocation and cost effectiveness. This advanced a architecture securely isolates each tenant data and operations in a hybrid environment of classical quantum and edge resources. Creating a seamless and protected user experience. Throughout this presentation, we'll explore how each pillar contributes to the transformation how their integration creates a powerful new architectural patterns for modern SaaS platforms. Now. Let's take a look at quantum computing revolutionizing SaaS platforms. Quantum computing represents a fundamental paradigm shift for the software as a sa service in short SaaS industry. I. It's an incremental upgrade, but an revolutionary leap that provides the tools to solve previously interactable problems. By integrating quantum com capabilities, SaaS platforms can evolve from being efficient tools for known process into powerful engines for discovery, prediction, and transformation, offering unprecedented value to the customers unprecedented computational power. Quantum systems provide the process information in a fundamentally different way from the classical computers by leveraging the principles like superposition and entanglement. They solve the complex problems and they can also explore a vast landscape of. Possibilities simultaneously, this results in exponential speed up for certain types of problems for a SaaS platform. This means being able to offer solutions to complex optimization challenges such as organizing global supply chains with millions of variables or designing complex financial portfolios. In minutes instead of years, intelligent insights. The ability of quantum algorithms to analyze high dimensional data allows for a deeper level of insights. SaaS platforms enhanced with quantum machine learning can identify subtle patterns and detect nos in massive noisy data sets that would overwhelm the even the most advanced classical ai. This could translate to a security SaaS that uncovers sophisticated, coordinated fraud attacks in real time. Our marketing platform that achieves true hyper-personalization span, understanding the complex customer behaviors on a new level, predictive capabilities. This new phone analytical power directly leads to forecasting with much greater accuracy. A new. Quantum enhanced SaaS could run complex simulations to foresee future outcomes with a higher degree of confidence. For example, a financial services platform could more accurately model market volatility and predict economic trends. A healthcare SaaS could forecast a spread of a disease by modeling countless environmental, social, and biological factors enabling proactive public health responses than reactive ones. Business transformations. Ultimately, the integration of quantum computing will enable the creation of entirely new business models and services. The impact goes beyond just improving existing applications. It's offering solutions that were previously in the realm of science fiction. A SaaS company could transition from selling software. To sending answers, offering optimization as a service or material discovery as a service that empowers business to operate in radically new ways. Fostering innovation and creating that will redefine entire industries quantum. So now if you take a look at the quantum enhanced Edge computing. The fusion of quantum enhanced edge quantum computing and edge processing creates a revolutionary distributed intelligence layer, fundamentally reshaping how applications deliver value. The powerful combination overcomes from the inheritant limitations of each technology, the physical distance to powerful centralized quantum computers are the limited computational. Capacity of local edge devices by strategically placing quantum enhanced algorithms directly where the data is generated. The architecture el eliminates the critical bottlenecks enabling new generation of intelligent real-time services. Redistributed workloads. This model represents a significant architectural shift. Instead of sending vast streams of raw data to distant quantum computer analysis, the workload is redistributed. Complex models originally trained on the quantum computer are deployed to a local run edge services. This means the sophisticated processing happens closer to the users. Closer to that data sources yielding transformative benefits, which could be minimized latency reduced bandwidth and real time quantum intelligence. So this is important as it tell us applications to leverage the analytical depth of quantum computing without the typical delay. A factory flow robot, for example, can use a quantum applications in all. To instantly adjust its actions rather than waiting on instructions from the central server. So novel applications part, take a look at the novel applications. The pot combination of quantum intelligence and edge deployment makes entirely new categories of application. Computationally feasible for the first time. The ability to perform complex analysis with near zero latency unlocks the possibilities that were previously confined to theory, real time optimization of complex systems. Imagine a city's traffic grid worthy edge devices in every intersection. Sorry IT devices in real section. Imagine. The city traffic grid where the, it's in every intersection can use the quantum algorithms to continuously optimize signal timing. Responding instantaneously to live traffic on device. Quantum machine learning. A mobile device or a smart variable could use a quantum machine learning model to provide truly personalized user experiences. Advanced onset simulations as an engineer at a construction site could use a ruggedized tablet to run the quantum powered simulations of material stress or environment factors getting intermediate. Highly accurate insights to inform the critical decisions on the spot autonomous decision making. This is the ultimate goal for many systems and autonomous vehicle. Liquid quantum enhanced edge intelligence could make more sophisticated, safer, and more ethical decisions in a complex, unpredictable environments. Now let's take a look at the evolution of the multi-tenancy models. So the evolution of multi-tenancy reflects a simple shift from being cost savings to a sophisticated architecture that leverages quantum computing to intelligently optimize shared resources while providing strong security. So basic resource sharing in initial stages. It involves like simple infrastructure partitioning, which will need to minimal isolation between the tenants, focusing primarily on reducing costs. Advanced segmentation, a more mature model that provides sophisticated and logical separation. While tenant share common services. Their data and processing workflows are kept securely isolated. Quantum Air architectures the most advanced stage where the architecture uses quantum capabilities to dynamically and intelligently allocate resources. It optimizes performance across all tenant boundaries, still wearing the tailored experience with economic advantages of shared infrastructure. Now let's take a look at the integrated pattern. Quantum federated learning. Quantum federated learning provides an elegant and highly secure solution to one of the biggest challenges in data science. How to train a powerful, centralized AI model without ever compromising on privacy of individual data sources. This integration pattern allows SaaS platform to build corrective intelligence. Learning from the distributed data of all its tenants while ensuring each tenant sensitive information remains completely isolated and protected within their own environment. So this process unfolds in a distinct cyclical pattern. The local quantum model training, the process begins with each tenant secure infrastructure. For example, tenant a, a. A hospital or a tenant, BA research institute both have their own private data sets. The SaaS platform provides it sophisticated quantum machine learning model to each one of them. Each tenant then trains this model locally on their own data. The hospital model learns to identify the patterns in its patient data. The research institute model learns from its experimental results. Crucially, the raw dataset never leaves the tenant secure perimeter secure model aggregation. Once the local training is complete, attendances don't share their data. Instead, they share their abstract mathematical learnings, the updates, or gradients from their respective models. The knowledge sharing is protected by the advanced quantum. Resistant cryptographic protocols. The SaaS platform acts as the secure coordinator gathering these encrypted model updates. This ensures that the shared information cannot be reverse engineered to reveal any underlying private data creation of an enhanced global model. The central platform aggregates the. Anonymized updates from all the participating tenants. By combining these divine insights, it creates a new global model that is far more intelligent, robust and accurate than any other individual local models that could be on their own. It has learned from e much wider and more varied pool of experience, reducing bias, and improving its spread to power for everyone. Distribution of collective intelligence. This enhanced global model is then distributed back to all. Participating tenants tenant A and tenant B can now use more powerful model for their own purposes. This directly benefiting from the collective intelligence of the entire ecosystem without having sacrificing an ounce of privacy. The quantum advantage in this pattern is basically twofold. It refers not only to the superior pattern recognition abilities of the quantum machine learning models used for the training. But also the advanced cryptographic techniques that provide a higher level of security for the entire process. Now let's take a look at the integration pattern. The customized quantum edge deployments. The pattern highly creates a highly adaptive, intelligent network that delivers customized performance by strategically deploying quantum capabilities to the edge. It allows a SaaS provider to cater to unique needs of each tenant while maximizing the platform wide efficiency. Tenant specific quantum algorithms. Custom quantum solutions are deployed to edge locations based on specific tenant unique requirements and usage patterns, intelligent workload distribution. The system smartly routes processing tasks between other local quantum edge notes and centralized quantum computers to ensure. Optimal performance and efficiency adapt to resource allocation. The quantum processing power is dynamically shifted across the edge network in real time, moving to wherever the demand is highest. The network is self-improving system, constantly learning from performance data to evolve and refine its deployment. Patents automatically. Now let's take a look at the integration pattern of the quantum machine learning. The pattern uses the QML to create a self optimize SaaS platform. Okay? This pattern that intelligently manages its own resources. It moves beyond reactive allocation to proactively participate the needs of every tenant. This process is a continuous loop, continuous monitoring the system, watches system wide performance and usage metrics in real time. The quantum prediction, A QML model and analyzes the data to accurately forecast future resource demands across all tenants optimal allocation based on the forecast, the platform automatically allocates infrastructure to meet the upcoming needs, ensuring maximum efficiency and performance. This creates a highly efficient system. That learns and improves over time. Ensuring resources are always where they need to be before the tenant even knows they need them. Now let's move forward to integration patterns, secure tenant collaboration. The integration pattern transforms SaaS platform from a collection of isolated customer silos into a secure collaborator ecosystem. It uses quantum technology to overcome the significant trust and security barriers that traditionally prevent companies from being working together. By enabling secure data sharing and joint processing at the edge, tenants can solve these problems by and build collective intelligence without ever exposing their sensitive data. Quantum Secure Data Exchange. So this is a, an exchange that goes beyond the standard en encryption. It involves quantum resistant cryptography to protect the data shared between tenants. This ensures that any exchange information is secure not only today, but also against the future threat of the quantum computers being used to break the current encryptions standards. Collaborative processing. So this pattern allows multiple tenants to pull the, their resources to, to tackle the common challenges using advanced quantum enhanced privacy. Pre preserving techniques, tenants can contribute insights from their data without ever revealing the raw data itself. For example, several competing retail companies could elaborate to identify large scale supply chain inefficiencies that affect them all. Industry specific knowledge networks. This builds on the first two points to create a spec specialized communities. The SaaS platform can become a host for industry specific consortiums, where the tenants can safely share knowledge, insights, and benchmark performances. Now let's take a look at the economic impact of the Quantum Enhanced SaaS. Now, if we take a look at this chart has a various comparisons of traditional SaaS and quantum enhanced SaaS. The economic advantages of SaaS have always been centered around shared infrastructure and economies at Square. Quantum enhancers simplifies these benefits through superior resource optimization, unprecedented computational capabilities, and intelligent workload distribution. Our analysis shows a significant improvements across all key performance indicators. When comparing traditional SaaS two quantum enhanced architectures. The most dramatic gains appear in the data processing capacity and predictive accuracy, where quantum algorithms provide exponential advantages over classical approaches. Now let's take away let's take a look at the key takeaways of an implementation roadmap for the companies. The quantum acceleration of SaaS is a transform IT opportunity that requires a deliberate and a phased approach. Leaders who successfully integrate these pillars of quantum computing, edge processing and advanced multi-tenancy will create an unmatched value. This roadmap outlines how to begin that journey. Phase one assess the current architecture. The foundational step is about understanding your starting point. Before integrating new technologies, you must perform a thorough evaluation of your existing platform to identify the most promising opportunities for quantum enhancement. Evaluate quantum readiness and analyze your SaaS platform to see where its current architecture stands. Identify the high value computationally intensive workloads such as complex financial modeling. Logical optimization or large, its AI training that are currently limited by classical computing map existing capabilities. Document your current edge processing capabilities and assess this sophistication of your multi-tenancy model. This map will reveal where you can most easily plug in your new integration patterns. Pilot integration patterns. The goal of this space is to start small, learn quickly, and prove value with minimal risk. Instead of attending a platform wide overhaul, focus on targeted pilot project that aligns with the key strategic objective. Select a single pilot. Choose one of the integration patterns such as Quantum Federated Learning or QML Driven Resource Orchestration apply to a well-defined problem where potential for it improvement is high. Measure and learn. The primary goal of this is to generate data and insights. Mely measure the performance improvements such as reduced latency or. Increased model accuracy, gather qualitative feedback from users and stakeholders to understand the real world impact scale and optimize. So once this pilot has demonstrated clear and measurable value, this phase focuses on expanding its success to. Embedding quantum capabilities into your platform's. DNA, expand and integrate the take the lessons learn from the successful pilot and begin a broader rollout. This may involve developing quantum native features and more deeply integrating the new architecture into your core product. Built in-house expertise scaling quantum capabilities requires scaling your team, invest in training and hiring. To build in-house expertise in quantum enhanced SaaS architecture, this internal knowledge is crucial for long-term success. Continuously refine the quantum landscape. Is there constantly evolving? Treat a paradigm. Treat your implementation as living system continuously refining your approach based on real world performance metrics. And emerging technologies. The path to quantum transformation is an evolution, not an overnight revolution. By starting today with strategic assessment and focus pilots, you can build organizational momentum and deliver measurable value while positioning your platform to lead the next generation of SaaS. Thank you all for your time and attention today. I hope you find the presentation informative and I'm happy to answer any further questions you may have. You can reach out to me at reach schrier kampala@gmail.com. Thank you once again, I.