Three. And by professional, I'm a data scientist and ai enthusiast, but more importantly, a builder by the experience. For over the last two decades, I have been deep in the trenches of data battling on-prem like the bottlenecks to navigating the cloud migrations. Implementing AI systems across different industries and now exploring how quantum integr land and photonic. Can unlock the dimensions of computations we have only dreamed off. If you have ever struggled to get faster insights from your data or if you have watched AI models play to due to compute constraints, this talk definitely is for you. I'm going to walk through the real world quantum algorithm, not just as a mathematical novelties, but as a game changer, as a game-changing tool. Of it for a problem solving optimization and innovations. Will it a technical yes, but it'll also be human because behind every algorithm is a decision. And behind every decision is submission. Let's equal that together. And by end of this session you will definitely walk away with a foundational understanding of quantum computing insights into the real world business cases, and clarity on where this. Field is headed to, and how can you be the product of? So now I little bit discuss about myself and you know a bit about me and let's talk about what are you going to walk away with in this next 30 minutes. I'm not here to throw a kind of like a expertise jargon on you. My goal is to make sure that every person who is watching this, whether you are a researcher, you're a cloud architecture product leader, or just a quantum curious walk away with. Something actionable and understandable. You will grasp how quantum computing truly differs from the classical systems and what make quantum logic so powerful. We will break down Shor's algorithm growers. Search and near hybrid approaches, and you will actually see in the production today, and from drug discovery to fraud detection. I will share with where quantum is already being piloted and where is headed because yes, some of. Or what you hear out there is hype, but a lot is a real, we will distinguish fact from the fiction. So you don't need to be a quantum expert to get this. You just need to be curious and open, and if you are, I promise that you will leave this session 10 steps ahead of where you started. The greatest enemy of the knowledge is not ignorance. It is that illusion of knowledge. I love this code from Stephen Hawing. And Stephen Hawing is now just about talking about the science. He was talking about human nature. We stop asking questions when we think we are, already know the answer, and that mindset has held back technology for that. Okay. Most of us have grown up believing that all computers work the same way. We process ones and zeros. They follow instructions and with more speed and memory, they solve the bigger problems. That model called Non Newman architecture invented in 1940s has taken us from floppies to the cloud computing and is the foundation of. Everything they use today, your phone, your laptop, even I models. But there is a cache. It has limits. And we have started to hit those limits now. And nature doesn't compute in ones and zeros. Nature is messy. Probably stick nonlinear at the atomic level there. Things are not just on and off. They exist in multiple states at once. They influence each other without touching, and they behave in ways that seems irrational, but are deeply mathematical. So that to stimulate the nature to model a molecule or understanding quantum physics or solve the certain optimization problems, we can't keep using classical logic. That trying to paint the ocean with a pencil. In, in, in 2019, world Quantum processor, smore performed a computational computation called random circuit Sampling is not just a useful problem. It is extremely hard for classic computers Second, or solve it in. In a 200 seconds, IBM argued their classical supercomputer could have done in two per five days. But even that is a stack difference, whether it was 200 seconds or 2, 2, 2 days versus 10,000 years, that the point is classical computing is not always the fastest tool. Sometimes it's not even the right tool as well. So its quantum computing does not replace the classic old computers. It. It compliments them by solving a new class of problems that rely on principles like superposition, entanglement, and interference. That's what we will explore today, what quantum algorithms are, why they matter, where they are being used, and how they will change everything from crypto. To the chemistry to ai. So let's first understand from the ground up, what makes quantum computing different and why Everyone from IBM to nasa, I. To startups like Zendo in investing in this space. Let's start with a simple classical computer, like a phone in your pocket or a server In the cloud is a built on bits, right? Each bits hold a value, either zero or one, just two options just like a live switch. So it's either on or off. This is the digital logic. We have used since every program, every video. Every AI model made of billions of those switches flipping on and off really fast. But the quantum computers use qubits, which is quantum bit. And these behave very differently instead of just being zero and one A can be both. Both at the same time. And this is called superposition. It's like a spin point, whether it's in the air or it's not head. Not tails, but it's both. Only when you measure it does collapse into one. Now imagine like you had two spinning points in front of you, but they are somehow entangled. Meaning whatever outcome you get, no one will instantly affect the others no matter how far apart they are. Einstein called this spooky action at a distance, and this is the magic of entanglement. When you combine superposition and entanglement, you can represent and process many states at once. A classical bait gives you one calculation at a time, but in quantum based. Cubits can represent two possibility at once. So that means two cubits, four state, three cubits, eight and 10 cubits already over 1,300 cubits, more state at the au atom in the universe. So now to be clear, this is not just magic. You still have to run a smart algorithm to extract the right answer. You don't just get all the answer for free. Quantum computing is not about doing everything faster. It's about doing certain problems differently. Problems like factoring factoring massive numbers, simulating molecules, optimizing delivery rules or training. Quantum enhanced AI models. Where the solution space is. So vast classical machines hit evolve. So classical computers is like walking through every hallway in a giant means, one at a time. Quantum computing is like entering all the hallways at once, but using interference. Veterans to cancel out the wrong paths and amplify the right ones. The key concepts to remember are here in this specific slide is superposition. Abit can be many things at once. In second is ments. Keywords can build link beyond the space. And number three is interference which is used to find useful answer from noise. So with these two quantum computer, don't just calculate the explore. Now let's go to the next slide, which is a quantum advantage. That, so that, now that we understand what makes quantum computing different, the real question is whether or where does it matter what problems? Actually solve better than the classical computer. The term we use is quantum advantage, which means that quantum computing solving problem faster or more efficiently than the best classic com computer supercomputer ever can. So even with the unlimited time and the memory there is not. Theoretical in 2019, Google claimed that quantum advantage for the first time using a random circle sampling task. While the usefulness of the task was debated, the milestone was clear for their specific problem. Google computers simply couldn't keep up. And let me break it down. Like simulation of those quantum systems and nature itself is a quantum. So if we, if you want to simulate in atoms, molecules, or materials using plastic computers is like translating poetry with a calculator. So quantum computers are built on the same physics, so they can actually naturally simulate chemical interactions, energy steers, and the molecule bonding things that require approximations on classical machines. This has a huge implications for drug discovery, material designs, and even the sustainable energy development. These show up and these shows up everywhere, logistics, finance, traffic systems, and even from machine learning, hyper parameter tuning as well. Classical servers like linear programming are logistic are fast, but they often hit the local minimal or something that will limit not the best solution, just a nearby one. So quantum algorithms like the quantum RO made optimization algorithm, which is qa OA expo exponent exponentially more options in parallel in offer offering a global solution ensures algorithm is developed in 1994 is perhaps one of the most famous quantum algorithm. It can factor large numbers at. Exponentially faster than the classical matters. And why does that matter? Because modern encryptions like RSA is based on the assumption that a factoring is hard. If large scale quantum machines becomes practical, encryption will break. This is why we now talk about post quantum cryptography, which is already atop. Top priority for the national security as well. And it's very important to clarify that quantum computer want replace everything. It is not going to run your Excel spreadsheet faster or replace your phone's processor. It shines very specific domains problems where the search engines grows exponentially or where Quantum Interference gives it is unique. Computational edge. Think about searching a for a needle in a haystack, a classical computer is checked each straw one by one. A quantum computer use uses growers algorithm to search the entire block or entire haystack in secure route time which means like problem within million possibilities can be solved in about 1000 steps instead of million. That's not just a speed, but that is about, you can say, a new kind of a like. Formula Quantum A is not universal, but it iss a profound when applied to the right kinds of problems As simulation, optimization and cryptography, quantum computer don't just offer performance boost, they offer is the only feasible path forward. Nowness, I will walk you through. A compute quantum of computer in didn't just appear overnight. It's a product of the DA Deckers of theory Physics. And engineering. Many of them are initially dis dismissed as impossible or just math. But here we are. Just a little bit of a history in 1981, this all started from a physicist, Richard. Famous man who asked a simple question in 1981, why can't we simulate physics on a computer? His answer, because computers are not built on the law of physics. But if we could build a computer that behaves quantum mechanically, he could simulate nature itself. And this was a seed and it launched a whole new field. Then came the major shockwave in 1994. Peter Shaw at Dell Labs. Created an algorithm that could factor the large number of exp expon exponentially faster than any classical algorithm. And this was not just a math breakthrough, it was a direct threat to the security of the internet. Suddenly quantum computing become not only a curiosity, but a strategy priority as well. Then love grower at Bell Labs, develop an algorithm that will search and sort databases in. Scale route time. This may not sound dramatic but in the computer science going from ON to the O scale route and is huge, especially when n is in the billions. So truly the early the field shifted from the theory. To hardware IBM, Intel and Startups like D-Wave began building actual quantum chips. But they were spec, they were like a skeptics, like the main challenge was CIC agile. They decor easily, even a breeze of temperature or vibration can corrupt them. So in 2019 will steam, published a paper claiming that they have achieved quantum supremacy, meaning that their quantum processor solved a problem that would practically impossible for a classic computer. While the use case was narrow, random circuit sampling industry milestone was massive. It, it validated the deckers of the world. So in less than four years, we have grown from chalkboard theories. Two functional computers processes and that lightning speed compared to the history of the classic computer. But remember, we are still early. We are in the NI SQ era where it's called NOIs Intermediary Scale quantum. The chip exists, but they are error pro and small and still the pace is accelerating really fast Now now that we know about the quantum computing team from let's look at the algorithms that are turning this theory into the real world impact. And let's, okay, a few on algorithms. The ones so you have seen how many algorithms? One is we have already discussed. One is the hor algorithm. In 1994, this one cracked the word open Shor algorithm factors the large intes exponentially faster than the classical computer. And why care? 'cause that's how RSA encryption works. The backbone of the internet security and if quantum computer, with enough stable qubit sixes, it would break RSA to 2048 in the days, while classical computers will take millions of years. So that's why governments are now investing in post quantum encryption. And then 1996 of growers algorithms search a un sort data database. It's like secure N times instead of n. That's a quad. Quad speed up there was like quad ready, speed up and in the fields like machine learning or bioinformatic, this can save a number of years in competition. Quantum approximate optimization algorithm QA oh eight is a hybrid algorithm is planned. The classic classical and quantum processing rate for the real world optimization tasks like the be routing and network. De balancing and even the financial financial mining as well. Vibration quantum, which is VQE, is used in the quantum industry, is estimates the ground, state or energy of the molecules. That means faster drug designs, material innovation, and even sustainable energy discovery. So there are not sci-fi ideas. They are already being tested on IBM ion q Extendo, and others. Quantum metal. We don't want replace classical one at all, but they will just unlock what classical core cannot reach. Use cases that, how is quantum computing being used right now? Let's look at some few sectors that are not just experimenting, but they're investing as well. One of the companies like pharmaceutical company, Roche and AstraZenca, are partnering with a quantum startups to simulate molecules and predict protein folding something classical computer can do at scale. Quantum help identify drug viable drug candidates faster. Potential, say billions in r and d and speed speeding up the time to market. In finance industry firms like Goldman SaaS and JP Morgan Chase are exploring Quantum for optimizing the answers portfolios and running Monte Carlo simulation more efficiently. These problem have expo exponential complexity. Quantum algorithms offer a better way to explore risk scenarios and logistics and supply chain. Volkswagen and DHL are testing quantum systems for traffic flow optimization and road planning, especially where classical computer or classical systems get overwhelmed. By too many variables. The even one minute of save time per vehicle can scale into millions in the savings in energy sector. ExxonMobil and BASF exploring the quantum chemistry to discover better catalyst for cleaner fuel production and energy storage can simulate chemical reactions that will take classical supercomputers centuries to approximate. Quantum computing is already used in narrow high impact tasks. Not to run your website but to reshape definitely r and d, your supply chain and your competitive edge. Most of the quantum computers used today, superconducting, cubics. The tiny electrical circles cool to near absolute zero, but there is another approach gaining traction is called photo toning quantum computing. So instead of electrons or ions, it's use photons. Practic, there was a kind of like particles of light to represent the qubits. Photons are naturally quantum. They do not interact with their environment easily, which means they are less prone to noise or any kind of like decoherence. So one of the biggest challenge in quantum hardware is like that un unlike superconducting cubs for Tony. Can operate at room temperature and use existing fiber optical infrastructure as well. And the same tech in our internet cables. So pump is x do in Canada and PSI Quantum, which is in the US and UK, are building the full scale quantum processor based on the photo photon photonic. Nic Alexander's Boreal system was the first. Photonic quantum computer to demonstrate the sampling of the scale. And this is very specific, very hard problem. But it proved that that photonic chip can are not just theoretical, but it's extremely practical as well. But it still face some challenges. Generating and manipulating single photons reli reliably is really tough. And building logic gates using light alone is not as mature as superconducting tech yet. So if we crack scalable photonic compute quantum com, we get faster, cheaper, and most robust quantum systems, and we can build them using the same optical com components. Already mass produced today. And now let's talk about what quantum computing can't you yet, because we are still very in early in any innings of this field. Cubis are very fragile. They lose their quantum state within microseconds. It is, this is called decoherence. So even the tiniest heat or for the vibration can throw them off. This is why Quantum error correction is one of the biggest bottleneck. To get one reliable, logical curate, you might need hundreds or even thousands of the physical curate. Today, quantum computers are called some of the devices, which is noise and power tolerance is missed. Devices, noisy, intermediate scale, quantum they work, they are not fully far tolerance yet. You can run small experiments, but scaling them is still a hurdle. Super connecting systems can need extreme cooling. Trapped I is needed trapped s lasers. Photonic chips needs precise light control. And all these platforms have strengths, but no one has solved the scale challenge yet. So most fundamental with them today are still explore theory but for many real word problems. We haven't yet proven a quantum solution that is truly better than the classical. So there are there are, there is also a high bubble. Some companies make quantum solutions that are not quantum at all. As researchers and builders we have to separate the science from the pr. So yes, the potential is massive, but we are not there yet. We are not in the charge g PT moment of quantum computing, but this is a D tech field. It's not just a finished product and that's okay. Is the internet. To doctors as well, to to come into the production. So now we have to talk where we are today. Now so where exactly are we? Quantum roadmap? The answer is early, but accelerating fast. So we are in NISQ era, which is a noisy, intermediate scale quantum. This means that we have real machines, but they are noisy, limited in the scale and not hot. Torrance yet. So not enough for full cryptographic breakthroughs, but enough for the experimentation. Hybrid models and early optimization trials. There was a commercial pilots alive. Big pharma like the pharma, finance and logistics are running parallel or limited powers to test world viability. And then we are probably five to 10 years from full scales for orange quantum system. But if you wait until then to start, you will be too late in this race. Now the quantum hardware landscape, let's quickly talk about what's under the hood, because not all the quantum computers are built the same. There are four main types of the quantum hardware platform. Number one is superconducting qubits, which is used by IBM, Google. They are fast but extreme but extreme cooling for the near term you can say. Experiment. Then we have a second tab is a trapped ion, which is used by Iron Q in Honeywell. More stable and precise, but slower GA speed. Then we have for Tony quantum computers like X extendo, A PSI, quantum, it's leveraged the light room temperature, great long term scalability. Then we have neutral atoms and topological qubits. Used by QRL and Microsoft. Still early stage, but promising for error resistance. So the race is not just over, we just don't know which architecture will win, but the diversity of the approaches is what make this space so dynamic. Here is what I will just a little bit about that. Where is still heading in the next five to 10 years? Let's break it down. So by 25, 22, 28. And then SQ maturity is that more stable? Ubes Hybrid quantum classical models become common in r and d environments. From 2028 to 2023 to 32 error bracket quantum computers, we will likely see the first 1000 plus large cubit system capable of be beating classical in real production and post 2032 quantum integration into cloud ecosystem, which will be like quantum will become a service embedded into AI and analytical stats. And you will call it from an API or like any other tool. So we are moving from lab experimenting to infrastructure grade tools like ai. The winner won't just be like those who build the tag, but those who learn how to apply it early. You don't need a PhD in physics to start building quantum application today. There are, these are tools. Many are free and I'll let you experiment right now in the cloud as well. One is what I. Personally use as well is IBM, which is a Python based open source and back with a real hardware on the IBM Cloud. IBM, quantum Cloud, and then we have a Google CIRQ. Great for the NS algorithms, especially the quantum circuits. Microsoft Q Hash as Azure Quantum, which is built for the hybrid quantum classical workflows. Then we have a Amazon bracket native is connected to the multiple hardware brackets. IQ Rage, JT and Fops Ford. And then we have a x. Spanning lane, which is focused on the quantum machine learning, integrates well with the PyTorch and the tensor flow. And whether the Azure data scientist developer, or just curious you can access the real quantum hardware from your act today. And I will say here's what I want to walk away with today in three clear takeaways. Quantum is not magic. It's a math plus physics and engineering and a blend of ai, but it change what's possible. Problems. We thought unsolved people are now being modeled. We are still early, but not too early. You don't have to wait for perfection to get involved. The learning curve has already begun. The future will be hybrid, classical and quantum AI and physics. Quote and light. And those who cannot connect, those words will definitely need now to start work on that. Because if you connect these all doors together today, that you will definitely, there will high chances of leading. And if there is one thing I hope this talk did is I hope that give, that this give you clarity, not confusion and curiosity, not kind a fear. And most importantly, a recent. As well. Thank you so much for your time and your patience, and thank you all. Thank you for being here. Truly. I know this is a complex topic, but the fact that you showed up tells tell me that you're not afraid of the complexity. You are here to lead the future, not just wait for it. So if to spark any kind of ideas, a question or even doubts let's stay connected. I share a lot on LinkedIn and I love. Stalking shop, whether it's quantum ai, data architecture, cryptocurrency, or blockchain or wherever is converging. So you can find me in the Sara Chori or directly as well from the LinkedIn messages. And you can check me on link Trace slash Sara Chori. Quantum computing is not just about faster answers. It's not is about asking the deeper questions and that's where the transformation begins. Thank you very much.