Hey all. Today I'm here to talk about quantum accelerated storage Systems. As enterprises data volumes grow exponentially. Traditional storage and processing systems are struggling to keep pace with the demands of realtime analytics, mission learning, unsecured the data management in response. Quantum computing is emerging as a transformative force redefining our enterprise store retrieve and process data. One of the most promising innovations in this space is the development of quantum accelerated storage systems. Let me introduce about myself. I'm go lead solutions architect solutions specialist in the cloud engineering area. I have around 17 years of experience specifically focusing in data engineering with a focus on cloud technologies and automations. I have consistently pioneered innovative solutions that bridge technology gaps and deliver measurable business impact. My career has been defined by developing cutting edge data pipelines, cloud architectures that have transformed operations across. Multiple service lines such as healthcare, insurance, and financial services. I speared the implementation of cloud-based data lake solutions that revolutionized how organizations can handle large scale of data and storing them accordingly. Let's move on to the topic the Quantum Max accelerated storage Systems. By integrating quantum processing units pus with I based speed memory systems and the classical infrastructure, the enterprises can unlock unprecedented performance for workloads such as realtime fraud deduction, large scale and training. Furthermore, quantum algorithms applied to data storage can optimize. Hierarchical memory usage, improve error correction, and enable more efficient data. Lifecycle management as a technology may choose quantum accelerated storage systems or poise to become em cornerstone of next generation enterprise data architectures offering not only speed and efficiency, but also new capabilities for secure, intelligent and scalable data ecosystems. Moving on the crisis faced by the enterprises in the current world and also addressing the storage crisis they're facing how to handle efficiently with the quantum computing storage. Modern enterprises are phasing and escalating data storage crisis, right? Fueled by the as you can see in the slide with the numbers, the explosion of big data, IOTA and realtime analytics organizations are generating petabytes of data daily. Traditional storage architectures designed for sequential predictable workloads are increasingly unable to cope up with the sheer scale velocity and the complexity of today's data. Thereby comes the limitations of traditional storage architectures, such as scalability. The bottlenecks, classical storage systems scale linearly and as therefore grows performance degrades. Adding more storage often leads to increased latency and management complexities. Number two would be inefficient data retrieval. Traditional indexing and retrieval mechanisms struggle with high dimensional, unstructured, or fragmented data. Number three would be constant energy consumption. Maintaining large scale classical storage requires significant energy and infrastructure investment. So the quantum storage, the paradigm shift, we could say, because this quantum computing offers a fundamentally different approach to data storage processing by exploiting. Quantum states and prob computation. Quantum accelerated storage systems can address the limitations of classical architectures through several key innovations. Moving on. Quantum computing the foundations for storage, right? There are several foundations. So we will go through briefly about it Now, number one would be quantum machine learning algorithms can significantly enhance quantum computing foundations for storage by enabling enabling the intelligent data. Coding, retrieval, optimization and management. These algorithms combine quantum computing parallelism with machine learnings, adaptability, helping create smarter, faster, and more efficient storage systems. So there are key QML algorithms, right machine learning algorithms relevant to quantum story systems along with how each contributes, such as quantum K nearest neighbors. Which is qk and N. The purpose of it is classify or retrieve data points based on similarity, and the storage use case would be answers the past and intelligent retrieval of data from quantum memory by identifying the most similar or relevant records. So the advantage would be offers a potential exponential speed up in high dimensional space. And the second would be quantum support Vector machines Q svm. The purpose of is finding the optimal decision boundaries in the complex data. The use case would be useful for categorizing stored data for compression, deduplication, or tiering. As we could say, as an example, art versus cold data. So the advantage, the kernel functions are computed exponentially faster than in classical SVMs, quantum resistant cryptography. It can be called a p qc. These are the frameworks are essential for securing data in the future where quantum computers may be capable of breaking traditional public key crypto systems such as RCDC and ECC. When designing storage systems, especially within quantum computing foundations, this frameworks ensure confidentiality, even quantum advers. So we can say as a breakdown of the main quantum resistant cryptographic frameworks you can consider is for secure storage systems, lattice based cryptography, code based cryptography, multi-variable polynomial cryptography. These are the main, mechanisms, we can use as a framework. Quantum Ling is a specialized form of quantum computing, right? Designed to solve optimization problems by leveraging quantum channeling and superposition. While it's traditionally used for general purpose cryptographic or data storage mechanisms, still it can be applied to optimized aspects of storage systems in quantum computing foundations. And the last would be quantum classical hybrid systems. What it is, it's a system where quantum computing handles parts of a problem that benefit from superposition and or tunneling. We can say the examples as optimization sampling. Classical computing handles IO control, flow, memory, and tasks not suitable for quantum processing. These systems do not store quantum data directly. That's the beauty of it. Since quantum memory is not yet scalable, but we can optimize on secure classical storage systems using quantum techniques. On what are the different case studies, right? In the quantum computing world to storage systems. So we go to explore about that. The case studies, the Fortune 500 enterprises are increasingly adopting quantum inspired algorithms to enhance their storage infrastructures, achieving transformative results in areas such as optimization, risk management, and data security. So we can see some notable case studies, right? Logistics, cybersecurity. So the company has integrated quantum inspired algorithms within its cloud services to optimize a logistics and bolster cybersecurity. By leveraging quantum techniques, company enhances its recommendation systems and strengthens data production measures, ensuring efficient and service operation. Across its vast e-commerce platform. Then either you're leading car company, which optimizes the traffic right? Company in collaboration with Microsoft has utilized quantum inspired algorithms to address traffic congestion by simulating vehicle movements and optimizing routing strategies. So company achieved a 73% reduction in congestion and an 8% decrease in overall commuting time, demonstrating the potential of quantum inspired methods in real world applications. Then the other leading technology company, which utilizes. Advancements in the risk management, right? Using the quantum. So it also partnered with Microsoft to explore the application of quantum inspired algorithms in risk management and financial services. By re-imagining the traditional modeling techniques, the collaboration aims to accelerate performance and redefined problem solving approaches. PAing the way for more efficient risk assessment methodologies. So in a nutshell, these case studies illustrate how Fortune 500 companies are leveraging the quantum inspired algorithms to drive innovation and achieve significant improvements in the storage and infrastructure operations. Moving on, so the quantum for storage tiering. Yeah, it's a tiering is a practice right in the storage world for organizing data across different types of storage media categorized by performance, cost, and durability. For example, in tier zero NVM versus CSSD, the cast would be some. But the performance would be ultra fast. The use case like the catching streaming realtime data. So there is other category in the tier one, which offers the example as SSD which will be fast, but not like tier zero, which is ultra fast. But we can use frequently access to files. And moving on to the tier two storage systems, where we can see the example as HDD and the performance would be a little slower, which can be ideally used for archives and backups. And the world has method as we know every, everyone like tape storage or cold storage, right? It's very slow, but for the can be used. So the challenge assign data object. Correct tier dynamically and efficiently. Okay. Now the quantum handling is a method for finding the global minimum of a cost function by exploiting quantum channeling and superposition, it solves the problem formulated as QUBO. So it's a quadratic, unconstrained, binary optimization and ING models. We can use. So these D Wave one similar systems implement quantum handling for large scale optimization problems. So the goal of quantum handling for storage tiring is to minimize the overall storage cost and the access latency by finding the optimal mapping of data blocks to storage tiers. So subject to constraints like performance requirements, capacity limits. On optimization. Applied. Basically, it's a quantum enhanced algorithms to reduce operational, financial, or resource related costs in more complex systems, particularly those involving large scale data and high dimensional optimization tasks. Qml combines quantum computing with classical machine learning to potentially outperform classical one approaches in specific problem domains such as portfolio optimization, supply chain, cost reduction, energy and storage infrastructure, cost management, cloud and compute resource allocation. Okay, now comes the question, right? Why we used to use quantum learning for cost optimization. Traditional machine learning or models are powerful, but often struggled with combinary complex spaces, exponential time algorithm, which is the best example for that. Optimization under constraints, spas, or high dimensional data. So quantum algorithms can offer through quantum parallelism, which is. We are discussing frequently about the superposition, faster convenience in a certain optimization scenarios, better sampling example for probabilistic cost estimation. So machine learning combined with quantum QL is a rescue area. What are the benefits of this? QL radio reduces time to reach cost efficient strategies, better modeling of real world systems. More accurate cost estimation and forecasting. These are the three benefits. Ideally, I could say right on top of the going on. What is a quantum resistant storage, security, quantum computers can break classical encryption. Algorithms, algorithms in the security of the classical encryption such as RC and EC can be efficiently solved with short algorithm data stored today might be decrypted in the future. So stored sensitive data needs protection that remains secured in a long term. So storage systems need to adapt to post threats for confidentiality, integrity, and. Oh, it can be implemented or achieved. So there comes the post quantum cryptography. Pqc. Cryptographic algorithms believed to resist quantum attacks because they rely on all mathematical problems, not efficiently solvable by quantum computers. Examples like lattice based examples such as crystal for encryption crystals s. And the LA as lattice based as a code based example is multi-variant polynomial based and ash based signatures. So these are some examples of post quantum cryptography application, how it can be implemented, encrypt stored data, authenticate users and device and secure communication between storage nodes. So these are the few applications, right of Pqc. Quantum safe, random number generation, so it is one of the security mechanism. QR entropy, randomness processes improve key generation quality and cryptographic strength. The third one would be quantum resistant authentication and access control. So there are certain protocols that verify users or device identity without relying on quantum vulnerable cryptographic methods. And last would be quantum watermarking. It's a technique to embed information. Into data or classical data protected by quantum resistant methods. It helps data, authenticity, ownership, and integrity in a way thats both classical and quantum attacks. On the quantum classical hybrid framework. So what is this? A framework, right? So hybrid system, it's an integration of quantum processors, as we discussed earlier, QPU and classical processes, which is CPU, and collaborative computation. The classical computer controls prepares inputs four and processes output from the quantum computer. Feedback loop classical algorithms, guide quantum circuits, parameters, quantum circuits provide evaluation results to the classical. So what is a classical interface layer? Then? The definition would be it's a layer, right? Responsible for coordinating and orchestrating interactions between classical and quantum parts of your hybrid system. Then what's the purpose of it? Yeah. Enable seamless communication, translating classical instructions into your quantum operations, and returning quantum measurement results back to classical algorithms. Then there is another layer or a framework. It's a problem translation layer. It is a layer that takes an abstract problem or algorithm specifications and transforms into your quantum computable representation, such as quantum circuits, hamiltonians or quadratic unconstrained binary optimizations formulations, which we discussed in the last slide, QUBO. The main purpose of it would be to bridge the gap between the problem domain and the Quantum's hardware operational model and the quantum processing layer. It is a layer that implements, runs and management of all these algorithms related to quantum by controlling quantum hardwares or simulators. So the purpose would be to perform the quantum part of hybrid algorithms, preparing quantum states, applying quantum gates, performing measurements, or running quantum enabling cycles. And finally comes the integration layer, the layer the dock states manages and integrates the various software and hardware components of your hybrid quantum classical system into your cohesive hold. So the purpose of this integration layer would be having a seamless interoperability between classical algorithms, quantum programs, hardware obstruction, and user interfaces, enabling complex hybrid applications to run efficiently, fastly, and reliably. So what are the implementation results right across the industry like healthcare, financial services, and manufacturing? So we'll take one, right healthcare. So quantum key distribution, which is QQED with the quantum resistant storage. So what is the use case in the healthcare industry, right? Secure storage and transmission of. Primarily the electronic health records, right? The medical records, EHRs, and imaging data, so it's an ideal use case, QKD and the quantum watermarking. For medical data integrity, the use case would be protecting medical images and genomic data from tampering, quantum inspired storage optimization algorithms. Which managing right massive multimodal healthcare data sets, genomic sequences, medical imaging, and patient history. This is one of the wonderful use case for the storage optimization algorithms implemented by the quantum. Then the lastly we could say securing multi-institution data sharing via quantum networks. So the use case, like the collaborative research right across multiple healthcare providers such as hospitals and universities, we can use the quantum networks in this case. Moving on to financial services industry. We could see 41% reduction in operation storage expenditures while processing with the four times more transactional volumes, maintaining consistent sub five millisecond retrieval latency for mission critical applications such as trading platforms using quantum enhanced encryption protocols, the manufacturing industry. As we know very well improving supply chain data integrity and traceability would be the primary objective in the manufacturing area. But the challenge is complex. Global supply chains generate vast amounts of data that must be accurate and verifiable. So what is the impact made by this quantum storage? So this quantums. Verify authenticity and prevent the tampering of supply chain data. Secure storage of traceability data supports compliance with quality and safety standards, so the result is enhanced trust in supply chain data integrity, reducing counteract risks, and ensuring regulatory compliance. So how can we implementation perspective, right? What is a roadmap for implementing the quantum storage? So I just segregated into the different phases. So the phase one would be the awareness and strategy where we'll be assessing the risks and needs, identify the sensitive data and storage workloads, conduct risk analysis regarding the quantum threats such as ours now dec later attacks. And educate the stakeholders, right? Train the IT team, security and compliance teams on quantum computing risks. Post quantum cryptography, which is pqc on quantum storage, technical technologies such as QPD, quantum water marking. Educate your team. And defend the objectives. Clear objectives with the goals. What are your goals like improving the data security? Quantum resistant encryption comes into play if you were, a goal is enhance traceability. Quantum watermarking is there if the goal is quantum, that means optimizing the storage efficiency. Then quantum in algorithms you can. Phase two would be the pilot implementation, right? Such as we can do proof of concept and planning. Select one or more pilot areas by securing the backup encryption using latest based cryptography, embedding the quantum watermarks insensitive files using the quantum handling technique to optimize storage tiering. So this is about the phase three. Execution, the evaluation, right? When you complete your proof of concept, implement the pilot, right? Encrypt store sample dataset using an quantum methods. And test it thoroughly. Using the performance latency, throughput, compatibility with existing systems, security under simulated threats and measure. The results have a clear defined metrics right to evaluate where results. So results always should be measurable using the encryption decryption speed, such as data retrieval, latency, storage, cost savings. What are the optimization use cases we have? And the last phase would be deployment, the enterprise deployment, right? In the deployment phase, the main challenge would be how to scale up the deployment. So you extend the quantum resistant storage methods across if you are using the cloud or on-prem storage. Extend it. I value data sets, right? Such as customer data, RD data, regulatory data. That's about the roadmap for the implementation. And yeah, after discussing briefly about all these quantum storage techniques and how it can be implemented and what are the advantages of it, so the key takeaways for the storage computing in quantum and what would be the next steps. So it's a strategic response to the emerging cyber threats. So quantum resistant encryption and quantum key distribution are essential to protect sensitive data from future quantum enabled attacks. And the other key takeaway would be quantum storage enhances data integrity and the auditability, quantum watermarking and quantum secure digital signatures ensure data. Tamper detection and traceability critical for compliance. Trust under digital forensics and quantum inspired algorithms, optimize the data infrastructure and also integration with classical infrastructure is a key practical quantum storage solutions today. Completely, we can say hybrid, right? Blending classical cloud edge storage with quantum resistant protocols or quantum assisted optimization algorithms. So standardization of quantum computing in storage and readiness are still developing. So NIST is finalizing a post quantum cryptography, pqc standards, early adoption positions, companies ahead of regulatory and competitive curves. Okay. What would be the next steps for organizations exploring quantum storage? Conduct your quantum readiness assessment pilot quantum resistant encryption. Collaborate with quantum vendors, right? And platforms engage vendors like p PQS Shield, I-S-A-R-D-I-B-M, quantum or Azure Quantum, right to access toolkits and support, join consortiums like Quantum Economic Development Consortium. We would say QED dash CE tsi, quantum Monitor standardization on policy development, train security, and IT teams design a roadmap. For scaled implementation, right? Start with the low risk pilot projects such as archival storage, backups. Yeah. These are the low risk, right? For any project or at the enterprise level. Start with it. Then plan for enterprise wide rollout. If you are successful with the pilot projects right at the low level you can make a wide rollout at the organization with quantum safe encryption. Security key management and hybrid optimization workflows. Yeah, that's it. Thanks for the opportunity to discuss about the different techniques, frameworks about the quantum computing in the storage systems. Thanks for all for your time. Thank you. Bye.