00:00:02Gabriel Fernandez: When you have an industrial warehouse where thousands of items go through each day and each delay, you pay thousands of dollars and if you have a bad day, you will pay millions of dollars, then you need to look into optimization with a very careful way. You need to use each new tool that the market can offer. And I was able to do this optimization with quantum, and we had excellent results on that.
00:00:35Murray Thom: Hello, and welcome back to Quantum Matters, where quantum computing gets real from D-Wave. I'm your host, Murray Thom. As we move past the hype and theoretical to explore practical real-world applications of quantum computing today and where the biggest opportunities lie in the future. Let's open the box and see what's possible.
At some level, warehouses are familiar to all of us, though their significance may be underappreciated. They quietly facilitate our daily lives. Every product we use has likely passed through one. It's where items are received, stored, separated, inspected, kitted and shipped, often under tight deadlines. Think of the metaphor of cooking in your kitchen. You take ingredients from the fridge, set them on the counter, separate them, combine them, plate them up, and then, send them to the table. Now, scale that up to an industrial kitchen with dozens of people sharing limited space, coordinating steps, and trying to avoid bottlenecks.
A warehouse works in a similar way with thousands of products in constant motion. Poor coordination would mean handling and rehandling items again and again. Wasted time, wasted effort. These are complex operations to optimize, and quantum computing is emerging as a powerful tool to tackle them as part of a practical engineering strategy. And so, today, we're going to look at how this works in practice. Joining me is Gabriel PLM Fernandez, lead scientist at the Wernher Von Braun Advanced Research Center. Gabriel, welcome to Quantum Matters.
00:02:02Gabriel Fernandez: Hello, Murray. Great to be here.
00:02:05Murray Thom: So, Gabriel, can you tell us about Von Braun Labs and the work that you do there?
00:02:08Gabriel Fernandez: Oh, yes. Von Braun is a nonprofit organization here in Brazil. We work with different algorithms. We work with different applications, but our main focus is to bridge the gap between science and industry, something very similar with what D-Wave does, but in a very different way. We work with semiconductors, we work with data. We work to be not competitors, but yes, facilitators. So, we create bridges in the sense. We use data to develop software and of course in the century where the size of data is bigger and bigger, to have new tools out of our hand as, for example, artificial intelligence is very important, but quantum computing represents a great step into the future. This is how we see quantum computing. This is how quantum computing is in our plans, a new tool to our toolkit.
00:03:06Murray Thom: And the work that you do there, tell us a little bit about that.
00:03:08Gabriel Fernandez: I developed algorithms. I work with many kinds of quantum technologies here, mostly focusing on quantum optimization. So, I have this experience with many kinds of quantum computer, different models of quantum computing, including both gate-based quantum computers and annealing-based quantum computers. I would say my work is more on putting real-world problems, problems that are relevant to industry inside quantum computer and deciding how can we use quantum computing to give us advantages on real industrial problems?
00:03:48Murray Thom: Excellent. Okay. So, you're creating an interface there helping businesses to bring new technologies in, helping them innovate their processes and smoothing that transition. Am I understanding that right?
00:04:00Gabriel Fernandez: Yes. Perfect.
00:04:01Murray Thom: So, Gabriel, I'm curious to know, how did you first get started working with quantum computing technology?
00:04:07Gabriel Fernandez: Oh, I have a bachelor's in physics. So, naturally, the quantum mechanics come to me. I never went to quantum mechanics. The quantum mechanics come to me, but I was fascinated to see how quantum mechanics can not only explain nature but also compute things that are of our interest. When I discovered the branch of quantum computing, I was fascinated in the sense because it allow us to compute things in a very different way. This itself is very enthusiastic, but as I were more and more into the field where I started to realize that we can have advantages using quantum computing that we do not have using classical computers, our common notebooks and smartphones, it made me want to dive deeper in this field. So, this was my first step into quantum computing after I have done my master's degree also in quantum computing, focusing on quantum algorithms on D-Wave quantum computers, and now, I'm pursuing my PhD on quantum computing as well.
But I would like to make it clear that I do this because I'm a quantum nerd, but quantum computers are getting easier and easier to access. And D-Wave is doing terrific work on making the quantum computers working on the hardware and at the development side. You have a very user-friendly approach. For example, on D-Wave's website, many examples where you can just press the button and you will start to understanding how you can go from a simple text lines of code to real applications on quantum computer. I myself learned a lot on this.
00:05:57Murray Thom: Oh, that's great. I'm happy to hear you say that. I'm going to pass that feedback on also to the software team. And I think that certainly, it was sort of intentional in the design from the perspective that software developers really rarely start a software project from a blank page. They're really beginning with some sort of example code that's attempting a problem that's as close as possible to what they're currently trying to do and modify it. And so, yeah, with those open-source examples, that's exactly the intent. Now, I remember you telling me there was something special about the experience of programming a quantum computer directly for the first time. What was that like?
00:06:31Gabriel Fernandez: Well, I remember the first time I programmed a classical computer, the basic hello world, and you were feeling that, oh, wow, the computer is doing something that I told him. When you do this with quantum computer, when you program quantum computer for the first time, you have a different feeling. It's like you were teaching nature something. And when I say, "Teaching nature," you are teaching nature in a very deep way because you are going into the atoms. You are going into the quantum mechanics. So, I felt this sensational, wow, I can choose the problem I want nature to do for me. I don't want to go into philosophy, but it's very interesting to think that we are made of atoms, and then, we are teaching atoms to do what these group of atoms want to do.
00:07:25Murray Thom: Well, I think that's an important connection to make because that's exactly what's happening. If we look at the history of technology through the Industrial Revolution in several different stages, it was a combination of using technologies. But to develop technologies, we had to be able to control, and build, and fabricate them better. And so, what's happening with quantum computing is we are actually getting engineering control over devices that are so small or so can find that their behavior is quantum mechanical. And a lot of the concepts that people talk about in quantum computing, like superposition, and tunneling, and entanglement, they'll explain them, but I think the common reaction is like, "Well, that's very weird." And it's weird because we don't interact with quantum systems when we're growing up, when we're playing with balls and sticks in our backyards and things like that. And precisely, as you're describing, the devices that we're building right now through those programming interfaces, are giving us a chance to actually directly interact with quantum systems. So, yeah, that definitely resonates with me.
00:08:26Gabriel Fernandez: This is something very relevant because when I was studying quantum computers and quantum computing, I have this list of phenomena that I cannot see in reality. I can read the book and understand what is superposition, what is entanglement, but it feels different when you are actually using it to do something that you want, not something that the book told you. You can take a problem from your area, from your field, from your daily routing and see a quantum computer solving it, using the behavior, the quantum behaviors that you have ever read only on books. So, this is very different.
00:09:04Murray Thom: And what's exciting, building off of what you're saying here, is that you are taking a problem... I saw you first at Qubits '26 presenting on this problem in warehouse optimization with gravity flow racks. You're taking that business challenge, and then, programming a quantum mechanical system to help you search for solutions and produce solutions very quickly in a quantum hybrid technology platform. Can you describe a little bit about the application and the work that you're doing there?
00:09:35Gabriel Fernandez: So, in this work I've made on warehouse optimization, we had a company that is a provider for Jeep, one of the big companies on assembling cars, cars, manufacturers, and they had a very huge warehouse, and they were having some troubles because when you have a big warehouse, an industrial warehouse where thousands of items go through each day, and each delay you have, you need to pay thousands of dollars, and if you have a bad day, you will pay millions of dollars, then you need to look into optimization with a very careful way. You need to use each new tool that the market can offer. And I was able to help to do this optimization with quantum, and we had excellent results on that. We had results that were very fulfilling for the managers of the warehouse. Even for our service on Von Braun Labs, we were scared because the results were so, so deep that we need to stop and breathe a little.
00:10:45Murray Thom: Yeah, that's so cool. I'm glad that you mentioned. This is a large automotive manufacturer. Everyone, I think is going to recognize the name Jeep, from their vehicles. And so, we know that the scale here of the industrial warehouse is enormous. And the thing is that when a large company like that is thinking about a warehouse as a component of their overall production and supply chain, they're thinking about how can I maximize the capacity of this resource and how can I also maximize the flow as it's passing through that vehicle? You talked about gravity flow racks. Maybe introduce that concept of gravity flow racks and why that's important for a warehouse.
00:11:23Gabriel Fernandez: Perfect. When you go to a supermarket, you have many racks. And between these racks, you have these aisles where you can walk and decide if you were going to the sector of routes or the sectors of equipment. But in an industrial warehouse, you want to maximize the space where you can put items. So, instead of using those racks that you have in supermarket that we call multi-level racks where you have different floors of items and you can go and take an item from the superior shelf or another item from the bottom shelf, in industrial warehouses, you have a very different system. The system is what we call gravity flow racks that is like many shelves glued together. So, you can put an item in the loading side of this system, and this item will slide through the shelf to the other side.
And then, when in the supermarket shelves, you could only put one single item in each shelf, one single box in each shelf, now, you can put many different items in the shelf. But this solution, although it brings many advantages because now, you can put more items and you will need less forklifts, and the forklift will need you to walk less corridors, you have a big problem because now, your shelf has a depth condition. And if you put the item A, B, and C, you will remove like C, B, and A. But let's suppose you have here your shelf and the production line says to you, "Okay, I need the B, not the C and not the A, but the B." So, what you will need to do is take the C out of this shelf, put somewhere, and now, you can take the B and bring to the production line. So, you have this constraint.
And of course in this simple example here, you had to take only one item that you don't want but you need to remove. But in the real case, in the real factory, you have dozens of different items here. And maybe you need to take off many, many items before you get to your specific item. These items that you remove it, but you will not use in the moment, what will happen with them? You need to put them back on the warehouse. So, this is a big work of reinsertion. This is the name, a reinsertion. But as this item was not required, you will need to cross the whole system to go to the other side, and then, you will load this item in another shelf probably. Of course, you will decide where you will put it. This is a whole new decision, but you will need to put it back on the warehouse.
00:14:13Murray Thom: Yeah. If you have more corridors and you've only got one item deep, then you know you can always pull the item that you just put onto it. But the difficulty is that then, you have lots of corridors in your warehouse that it reduces the density with which you can store things.
00:14:25Gabriel Fernandez: Perfect.
00:14:25Murray Thom: And then, you've got all that complicated routing moving through those different corridors. So, by basically using these deep racks where you can put multiple items, you get the benefit of the fact that you can store a lot of things in the item, you've got greater density in the warehouse, but now, you've got a new challenge. And the challenge I think probably arises from the perspective that it's not the naive scenario where there is exactly one rack for every product, right? You've got a lot more products than you have rack space. And so, effectively, what you're trying to do is you're trying... Even if you did, if you mistakenly put something into the rack in the wrong space, well, because of the gravity flow, it's going to flow to the front. So, now, you have to take into account that that item is going to be between other items.
So, what you're doing is you're kind of saying, "Okay, let's embrace that operating condition, and then, let's figure out, okay, what's the best way to operate that when you're going to have items in a sequence?" So, how did you tackle that? What was the right way to approach it?
00:15:16Gabriel Fernandez: Oh, perfectly put, Murray. These racks, they have deep shelves. So, if you have an efficient way to allocate new items on the shelves, you will spread different items across many racks. Imagine in a supermarket, you go to buy some fruits, and if you look to your side, you have a wire or something completely unrelated to your fruits, and you start wondering why. If you don't have a very good allocation strategy in a warehouse where millions of items will go through in a single month, even in a single week, you will get this awkward situation where you ask why these items, these type of items are together with the other type of items? And this was actually the problem that we found. We realized that what the factory, the warehouse was lacking was a very efficient strategy and what was the problem that they were getting?
They had mixtures of items that were only disorganized in the factory. In some moment, the items will be mixed because there are not sufficient enough shelves to do one kind of item here, another kind of item there. So, what was the strategy that we planned? What was the strategy that we built on the quantum computers? The strategy was if the mixture is something that will happen, we will start not seeing this as a problem but as a solution. So, we imagined and we developed a way to, since the beginning of the operation of the warehouse, to allow these mixtures, but these mixtures were done in a way that when you remove an item, you will probably have another item that the production line will ask for. For example, when you are building a car, you also have the lights. If you need a left light, you will need a right light. There's no need to separate them. Put them together, but in a way that when you get a left light, the following item will be a right light. So, it's complimentary.
00:17:32Murray Thom: So, you're using the quantum computer to optimize how the items are put onto these racks. Is that right?
00:17:39Gabriel Fernandez: Perfect. I like to imagine the game, I guess in English, the name is Tetris, correct? Oh, in Tetris, what you have is different type of items. Let's suppose you think, oh, I'm going to put this right one, the vertical ones all together in the left. If you do this, you will lose the game because the item, they have a specific weight should be put together so you can maximize the space. It's not that exactly on a warehouse, but it's similar. You need to think of the mixtures that you will have.
00:18:10Murray Thom: Right. Right, right, right. Okay. So, Tetris, you've got these objects that are falling down. You see an object, you're deciding on its place. And one of the features about Tetris is that each of the items that you get, you get them one at a time. Is that the same kind of process that you're seeing in terms of optimizing the placement of each item as you receive it?
00:18:29Gabriel Fernandez: This is the whole point. You got to the point. When you have Tetris, you actually take one item, and then, you allocate. When you have a warehouse, usually, warehouses work in the same way. They got an item, and then, they insert this item. Then, you have another item, and you put the other item. So, you are making these local decisions based on only one item.
Quantum computers allow us to work with many items in, I will not say, "In parallel," but it allows you to work with many items in a single step instead of deciding where I will put this item, and then, the other item, I do in a single decision because it makes sense. For example, when we got home and we went to the supermarket and we bought a lot of stuff, we will put it on the kitchen. What people usually do is to take all the items out of the bags, and then, decide based on all the items. So, you will have a global decision, not a local decision. In the warehouse, in our strategy, we do the same way. We look all the items and we do a global decision because if you do the insertion in one item, and then, the other item, and then, the other, you have the risk that you will put the first item in a place where the second item would look fantastic.
So, what we actually do is to look at the whole problem, all the warehouse. So, you have data that can show the whole warehouse, and then, we decided based on the status of the warehouse and the number of items and the type of items await in allocation, we decide in a single step, the whole placement.
00:20:13Murray Thom: What were the results that you saw?
00:20:15Gabriel Fernandez: The results were terrifically good because we compared these different strategies where you put one item each time and where you put many items together. You do this decision all together, when we use our strategy and put one item after the other item. Because we use a secret sauce in the parameters we use on our strategy, we can see the warehouse becoming more organized, so less insertions in a month, for example. But as we increase the number of items we allow to go to our decision together, the number of reinsertions fall even more. So, just to give a sense of the results that we got, in a simulation of three months, we got 90% less cost in a trimester than what the factory was having before. So, it was a nice result because 90% less on operational costs is a really big number.
00:21:18Murray Thom: Huge, huge number. That's phenomenal, right? You're taking to account this approach, coordinating the way the parts might be placed relative to one another, and then, looking at a whole set of them and sort of deciding how those are going to get shuffled through the warehouse. What was the reaction like? You're working with a real company, you're doing a study on like three months of their operations and you're showing them, okay, this is effectively a 10 times reduction in the number of reinsertions you're going to have to do with these parts. What was their reaction when you told them that?
00:21:45Gabriel Fernandez: Oh, the reaction was, "Let's do this simulation and let's do it on the real world." We are working on it. We are working to put this into the real factory. Of course, there are a lot of possible sources of errors. The simulation, it already takes all the source of errors and put on the code. So, we have a simulation that is very similar. Of course, when we are talking about the real world, it gets a little more complicated to put some strategy into this full warehouse where you have many trucks and where you have many problems. So, you need to think carefully on each step, but the reaction was priceless because of course, this is a very new idea because I met a guy on Qubits '26 and he told me that the supply chain is called supply chain because you need a chain of operations.
Our solution allow us to link the warehouse, the allocation decision into the production. So, you produce exactly knowing where you will put, so it's all together. So, this allow us these very specific changes. For example, let's suppose you are not producing, but you are shipping these items and a truck will deliver it into your warehouse. What you can do is when the truck is in your parking, you are calculating the allocation. We have done simulations where we had hundreds of different items and we were able to get solutions in five seconds to hundreds of items. So, even before the truck stops, you can have a solution and say, "Okay, you will put this here, this year, this there."
00:23:29Murray Thom: That's fantastic. I love that perspective of you know what's getting produced at the factory, but because of the speed with which you can do these calculations, you don't have to pre-calculate the day before. The truck can get packed, it can get shipped, it can be on its way, and you don't even have to worry about traffic. You can just wait until it drives on the lot, then kick off the optimization. And by the time it parks at the gate, you've got your plan for how you're going to be unloading and moving things throughout the warehouse. That's phenomenal.
00:23:55Gabriel Fernandez: Something that we learned is that local decisions are not the best. We know this intuitively. When we are choosing the outfit that we are going to wear to an interview, for example, you do not choose first your T-shirt, and then, your pants. You choose it all together because what is important is the all-together stuff. So, if we can bring this to different fields, we will see good results arriving. And quantum computing, because the properties that allow us to explore many decisions in a single step, many, many different configurations in a single step seems for me, one of the correct tools to do that. And something that I want to bring here is that quantum computing is not AI. It's very different. It's very different. They can be complimentary. And I think the situations will arrive more and more into the future that quantum and AI together are the solution. And I'm not even talking about quantum matching learning, but just the quantum more AI.
00:24:54Murray Thom: Yeah, I agree. And I see the business usage pattern where you might be using artificial intelligence systems to help comb over historical data and make recommendations about, let's say, let's do some promotional pricing on this particular product. But then, once that pattern and recommendation has been made, then you have to decide, okay, well, what's the right combination of business decisions across markets, and products, and business constraints? And that's a complex multidimensional optimization problem. And that's really where artificial intelligence systems run into their limits, and that's where quantum computing really excels. So, I think there are absolutely cases just like what you're describing in terms of like a forecasting, and then, operational handoff between those two systems as well as them working together.
There's one other comment I'm thinking about, which is that you were mentioning about linking steps in the supply chain. That is part of the reason that classical computers have struggled with that is because that linking really expands the solution space and makes it interconnected, which in a way that classical computers find hard to search. They're very fast. They're very efficient at what they do with checking solutions, but even checking billions of solutions in a second is a small neighborhood relative to the full solution space of all the different ways to configure a warehouse. So, I think this opportunity to use this quantum hybrid computing platform to be able to start taking business information about relationships and incorporating it into these operational steps is transformational. What would you say are some of the next steps for this optimization project?
00:26:30Gabriel Fernandez: I guess to connect different parts of the warehouse to a single decision. It would be very fascinating for me at least to see a whole factory working on quantum hybrid solutions. Or not solutions in the plural, but a single solution to the whole process. This would be something to think about because we would not be able to call it supply chain anymore. We were erasing the chain. It's just one solution. This would be very fantastic. I guess as we are more and more advanced in quantum technologies and in quantum hybrid technologies, we will see more possibilities coming to the problem. When they decided to put the name, supply chain, they were not even thinking about that in the future, we would have tools that would allow us to explore and analyze all this data that we have today, and more than that, integrate it all in a single solution.
So, there are many, many topics, many fields where this may be possible. Maybe finance, for example, maybe chip design is another example. Maybe the people that are watching us are thinking in examples that we will never think ourselves. So, it's a whole new space for creativity. It's a whole new space to each one of us developed our own solutions and bring to our own daily basis, the solutions that we are thinking about.
00:28:08Murray Thom: Yeah, I love that. Sort of moving from optimizing supply chain management to maybe a new term, optimizing supply network management, recognizing that it works as they are, but actually being able to treat them the way they exist.
I certainly run into a lot of folks who are approaching quantum computing for the first time. You've been working with this technology for some time now. If you were going to think back to when you first started, if you were going to give yourself some advice, that actually might be really useful to folks who are maybe themselves beginning their journey. What piece of advice do you wish you knew when you had started with quantum computing?
00:28:45Gabriel Fernandez: When I started with quantum computing, I went through the books. I'm this kind of person. I like to read books. I like to read. But today, there are so many tools and through these years, I were able to use different tools and realize that if I had the tools before, it would shorten this whole journey of years that I've been studying quantum. For example, D-Wave has a series of courses that I had the opportunity to make when I already understood a lot and the feeling that I got was, oh, if I had the opportunity to do these courses before, it would be very less painful. Not that was a painful process because when we do something that we like, it's not painful at all, but it would be very easier.
So, I suggest going into videos, going into D-Wave official YouTube channel, going into researchers. Researchers all around the world are trying to make you understand why quantum is so important. So, feel free to ask. Feel free to contact me even. Feel free to give a look at the presentations that D-Wave have. Feel free to look at my presentations at Qubits '26. So, yes, there are a lot of tools.
00:30:05Murray Thom: Well, for anybody who's looking for more of those resources, they can also take a look at D-Wave learn. If you just do a search engine search for D-Wave learn, you'll find more resources in that direction. Gabriel, it's been great having you on the podcast and getting the chance to learn more about the amazing work you've done with D-Wave's technology. Thanks for being a part of Quantum Matters.
00:30:23Gabriel Fernandez: Thank you, Murray.
00:30:26Murray Thom: That was a great conversation with Gabriel. What really stood out to me was his enthusiasm both for the technology and for its practical use in warehouse optimization in a way that the operators themselves can see themselves using. I learned a lot about warehouses, the use of gravity flow racks to allow the warehouses to have more capacity and also the fact that that introduces a new challenge, optimizing the placement of products arriving into the warehouse onto those racks in order to have the flow through the warehouse operate better. It was fantastic and fascinating to me that he's gotten it to the point where between the time that the truck arrives on the lot and parks at the gate, he can optimize all of the placement of the parts and the products that are in that truck. That's phenomenal.
So, that's it for today's episode of Quantum Matters. Thanks to you, our viewers and listeners, for joining me. Please follow so you don't miss an episode and to learn more about how D-Wave works with organizations, to get started and succeed with quantum, visit dwavequantum.com. Until next time, I'm Murray Thom. Stay curious about your quantum reality.
