Hi everyone. This is Anush n. I'm a software engineering manager at ai. I have a couple of articles and done some research on Edge ai. Today I'm going to talk about how quantum computing is supercharging edge AI and how it impacts the processing power size of the neural networks. Power usage, latency, and cost. By examining some of the real world deployments across multiple sectors, we will talk about how edge AI technology is revolutionizing embedded systems through improved processing efficiency, reduce operational cost, and enhance security measures optimized through resource utilization. Let's talk about the scale of a challenge. By 2027, it's projected that there is going, there are going to be 75 billion iot devices in the world, and such massive data volumes at edge is going to be difficult to handle. With quantum enhanced ai, we can have a 3.2 x boost in the processing capacity and a 41% reduction in operational cost. The complexity of the edge data at such a high resolution video. Medical scans or autonomous vehicle telemetry will require faster and smarter processing. As you know that the classical systems need to keep up with the demands like latency, power and scalability. So deploying quantum enhanced techniques in these fields is going to exponentially get better results. Then the classic systems. Let's talk about a more hybrid approach Through this problem, we have a quantum solution and a classical one, but this is a hybrid one, which is not purely classical and not purely quantum. It's synergy. The quantum algorithms can handle. Optimization heavy and very high dimensional tasks like routing, scheduling, or training complex neural networks. Classical systems can manage routine or deterministic tasks like IO operations or simple control loops. The outcome of all these neuron networks can then be processed with a 3.2 x more parameters while still meeting the sub 15 millisecond latency. Comparing all this to classical only systems, which plateaus at 1.4 x capacity under the same constraints. Now let's talk about some of the improvements of using quantum. These are some of the numbers you are shared wherein you can see there is a 78% faster optimization solving. There is also a 67% reduction in power consumption. And a latency which has been reduced to 30 millisecond compared to the one 20 millisecond baseline, which itself is a 76% improvement in the overall latency parameter. The model deployment speed is also increased by 3.2 times with the quantum enhancement. So these are not just real world impact, but these are. Also, these are not even like lab results. These are actually impacts that are having on the real world case studies and scenarios that we have seen. They are feasible in the industrial automation, autonomous vehicles and infrastructure domain. The reduced latency and increased deployment speed can create an edge AI infrastructure that is both agile and powerful. Now let's look about some of the case studies shown here. In the manufacturing enterprises domain quantum algorithms can be used in iot networks. For achieving a better anomaly detection of 82% and a reduced false alarm rate by 43% in the healthcare domain. Quantum algorithms have been used in medi medical imaging algorithms, which has a real time diagnostics with 91% accuracy. And we also have a maintain HIPAA compliance with all this implementation. For a smart infrastructure, quantum algorithms can be used for dynamic energy distribution. We achieve 37% of power savings and faster response time with our quantum classical systems. So in every vertical that's tested, the quantum classical systems have outperformed the traditional ones. Now let's look at some of the neural network processing capabilities of the system. The classical system actually hits a wall with only 1.4 x capacity, which is actually increased if possible, before latency becomes unmanageable. This forces the developers to compromise between complexity and real time performance. A hybrid model supports 3.2 times greater capacity without latency sacrifice. And how this can be achieved is through quantum parallelization efficient navigation of our systems. In the enhanced quantum ai, we also provide a high dimensional parameter specs. For this navigation, a superior performance in spatial temporal pattern recognition like movement tracking, video analysis, and many other use cases. Now let's look at some of the resource allocation optimization. Techniques. So quantum units can be optimized across compute, memory, bandwidth, and network storage. So the quantum networks actually reacts to changes under 30 milliseconds, which are 4.2 times faster than the classical methods. For example, like in the Smart Cities case study that we looked upon, it is adjusting the power allocation and the autonomous vehicle fleets case study wherein it actually helps to manage bandwidth and CPU dynamically. So the optimization pipeline goes through four different steps. First is getting the quantum algorithms detect the complex dependencies. Next is to explore the millions of possible configurations in parallel. Then we execute with precision and control and also predict and adjust according to the conditions that evolve. Now let's talk about some of the security and cost benefits the quantum enhanced technology can be used to for cryptography in the following ways. We have a lattice based key exchange for managing all the encryption. We have a temp tamper evidence signatures implemented, and also multivariate polynomial encryption. The operational cost of these systems have gone down by 41% because of the quantum machine learning algorithms that are being used. It also uses less power and less hardware, which requires less maintenance. The reliability of the system has really gone up with a 99.99% response time with AI driven fall detection. Self-healing infrastructure and smart redundancy systems. So this isn't just faster, it's safer, cheaper, and more reliable. Let's go through the implementation roadmap for this quantum. First, we review the current infrastructure and identify where quantum can be value added, for example, where we can optimize the tasks. Next, we can test the quantum inspired algorithms on classical hardware and validate the performance boost for integrating the quantum algorithms in the hybrid quantum classical workflows. We obviously would optimize task delegation and data flows, and the system would scale at a enterprise wide, and we would be monitoring feedback loops for continuous tuning. So this roadmap enables gradual low risk adoption with incremental gains. Let's talk about some of the cross-industry applications mentioned here in the autonomous vehicle domain. We can get quantum to make the sensor fusion faster by 2.7 times. There is a 58% less power usage, which would be beneficial in the longer range and a better path planning and real time decision making. With smart cities, we have seen a 43% reduction in traffic congestion and a 30, even seven, 37% improvement in energy efficiency, which also helps in faster emergency response time. In the industrial iot domain we have seen a 67% drop in downtime. Via productive maintenance, there is an 82% more efficient production scheduling. So overall, we can see that the enhanced edge AI is not specific, it's to the industry. It's also transformational across sectors. Lemme go through. Let's look at some of the future research directions that we can see like here. So first is the quantum hardware that we have to get to. So we do need a domain specific, hardware for these particular edge devices. We also see a distributed quantum processing especially like a quantum cloud in the edge, and then we have quantum neural networks. Now, this is an entirely new AI paradigm that has to be looked upon too. Now these developments could multiply today's gains exponentially. Even now we are seeing that this is just a tip of the iceberg. This is a long-term technological revolution. So thank you for listening to my presentation about the quantum enhanced edge ai. If you have any questions, please reach out to me on from my LinkedIn profile, and you can have a study of all my. Articles on edge. Thank you and have a nice day.