On behalf of OTC Markets, we are very pleased you've joined us for our Global Technology Conference. The next presentation is from Zapata Quantum. Please note you may submit questions for the presenter at any time. You can also view a company's availability for one-on-one meetings by clicking Book a Meeting. At this point, I am very pleased to welcome Sumit Kapur, Chief Executive Officer of Zapata Quantum, which trades on the OTCQB Venture Market under the symbol ZPTA. Welcome, Sumit. Thank you, Lily, and thank you all for joining today. I am really excited to be here and excited to share with you so much about the exciting things that we're doing at Zapata. It is an extraordinary time to be in quantum, and as I hope you'll see, it's an extraordinary time to be in quantum software in particular, as the space continues to shift towards software from hardware. We continue to drive value creation and utility from all of this exciting technology. I will move on into the presentation. Before I talk about the company and about the exciting things that we're doing at Zapata, building on our legacy of really leading the software space in quantum since 2017, when we spun out of Harvard's Quantum Lab, I want to give you all a little bit of context on what is going on in the quantum space from our perspective. Really what we're seeing is a shift from quantum advantage, which we have now proven, and is showing up in the form of a great deal of hardware technological progress towards quantum utility. Now we're at a point where we actually have to move forward and take these amazing capabilities that are coming in the form of these quantum processing units and actually derive value from them, and that's really where the exciting things happen. If you look back at an analogous industry, obviously AI, GPUs created a great deal of capability, and they were around for some time before we saw the transformative value creation come in the form of their application to first Bitcoin mining and then towards large language models and training of AI models. Really that's the exact same shift that we're seeing in the quantum side, where these capabilities exist at some level, and are being scaled as we speak. We're now moving towards the discovery and development of these applications. So there's a handoff there, and it's a handoff that we're particularly excited as we shift towards emphasis on hardware, to emphasis on software. The slide here talks about the executive order that came from the Trump administration just a couple of weeks ago. That executive order was about continuing our leadership in frontier sciences, particularly quantum. In the executive order, the executive branch established the QCADDS effort, which is the Quantum Computing for Application Discovery and Development Science effort. We were really excited about that particular development, largely because the words that were used in the development, the application discovery and development science, are the exact same words that are used in our mission statement. If I move forward, this is our mission statement at Zapata. As I said, we spun out of Harvard's Quantum Computing lab in 2017, and our mission since then has been to dramatically accelerate the discovery and development of quantum applications. You'll note that it's the exact same words. Obviously, we couldn't get a better signal of validation for our thesis and our mission statement than what came out of the executive branch. We really do think that the discovery and development of these quantum applications is what's going to be really exciting. Over the next few minutes, I'd like to explain why. First, I'll explain a little bit about why we believe software will ultimately create a disproportionate share of the value in quantum computing versus hardware, where a great deal of the attention is being paid right now. Secondly, how eight years of pioneering work in the field has uniquely positioned Zapata to lead that effort, an effort that we've laid much of the foundational work in. Third, why we believe that it creates a very compelling opportunity for investors like yourselves. Without further ado, I'd like to say a little bit about Zapata. We are the only U.S.-based, publicly traded quantum software company, and we're a leader in the space. As I said, we spun out of Harvard's Quantum Computing lab in 2017. Since then, we've done some of the most foundational work in the space, with 60+ foundational patents in quantum software. We've done 40+ peer-reviewed scientific papers. You see the logos there on the right. We've done work with the best and brightest on the public sector side and the private sector side. Notably, for example, with DARPA, we led all three phases of DARPA's landmark quantum benchmarking program, which laid a foundation for much of what we do today. We also worked with a host of Fortune 500s, including BMW, BP, BASF. I'll talk a little bit about our work with Dana-Farber Cancer Institute that was recognized as one of the top 10 papers of 2025 by "Nature Biotech," which is really the most prestigious journal in the biotech space. Really, we've done a great deal of the most profound work in quantum software, I'll talk a little bit more about that. To lay a little bit more of the backdrop, we do believe that quantum computing has reached an inflection point. I'm sure many of you follow the quantum computing space. It's now clear that quantum is going to be the next technology super cycle. McKinsey estimates $2 trillion of economic value by 2035. Based on that, a $100 billion platform TAM that we're going after. There's an arms race in the public sector, as you can see, investments from governments including Europe, Asia, and the U.S., as each continent vies for supremacy in quantum. BCG estimates 90% of the value will accrue to the 10% early movers, which is driving a great deal of inter est and engagement from the enterprise side that we're the beneficiary of. You see some quotes from some of the tech majors. It's now clear that the quantum really is going to be the next tech super cycle, that's something that we're excited about. One thing that needs to be appreciated is that as with other technology cycles, it really is software that's going to drive value creation ultimately. If you look back at the PC mobile tech super cycle, obviously it was Windows and iOS that drove a great deal of the value and then ultimately captured that value. On the web side, you had Google, AWS, and Microsoft Cloud. The cloud providers come in and create the applications that really unlocked value from those web capabilities. Finally, on the AI side, obviously OpenAI and Palantir are software platforms. Even NVIDIA, which is a hardware platform, their dominance is driven by the dominance of CUDA, their software platform. Really it is, in all tech super cycles, software that ultimately drives value and captures a great deal of the market caps. You see the trillions of dollars of market cap that really accrued because the software value was created. We think that it's going to be the same in quantum as well. Currently in the quantum space, it is dominated by hardware and full stack efforts, that's leading to what we believe is inefficient software progress. I'm sure many of you are familiar with the names on the right there, IonQ, Quantinuum, D-Wave, Infleqtion, Rigetti, Google, IBM, IQM. These represent companies that are vying for a lead in their particular paradigm in terms of hardware for the quantum space. Each is focused on improving out their technology, what this creates is a collective action problem with respect to software. The way that we think about the stack in quantum is really a why, a what, and a how. The why is the use cases that you're solving for in terms of the problems like drug discovery, encryption, optimization, simulation. We'll go through some of those. The what is the algorithms that you use to solve for those use cases, the how is the hardware. There's a great deal happening in the how, in the hardware layer, there's less work and progress being made in the why and the what, that's really what's going to be driving the value creation ultimately. As a result, with this competition between hardware companies and hardware paradigms, enterprises are very cautious about committing to one particular paradigm. When we talk to enterprises, they are reluctant to commit to work with one of the providers you see on the right, because if they end up working with that provider and it's not the winning paradigm of the 20 or so that are vying to be the winner. Then they've locked themselves into a platform that isn't going to be resources well spent. They're reluctant, as opposed to us, we come in and we're hardware agnostic, that's a real value add, and it allows the enterprises to develop applications that are actually resources well spent regardless of where the ultimate winner is in terms of the paradigm. Ultimately, what we're seeing is inefficient progress on the software side and much more progress on the hardware side, and we solve that by coming in and providing a hardware agnostic approach, which is what we've done historically. Here, I'd like to talk about a paper from Google that came out in December of 2025. This paper really underscores many of the points that I'm making, which is that we're seeing a great deal of progress on the hardware side, but much less progress in software. They actually talk about the collective action problem where you have hardware players all focused on driving progress on the hardware side, but they see something like software as a common good, they don't work as much on the application layer. It ends up being an under-resourced and underutilized part of the stack, and that's the place where Zapata has focused and will continue to focus. To give you an idea of what that looks like in practice, Zapata has been and continues to be the leader in quantum software across high-value domains. T he way that we break down high-value domains, we look at it in two camps. There are quantum native applications, which are using quantum computers to solve problems of quantum physics, to be able to simulate quantum physics. This is what Richard Feynman had in mind when he proposed the quantum computer some 50 years ago. Then there are quantum mapped applications. Quantum mapped applications are using quantum computers to solve an otherwise intractable problem, but map it onto quantum physics. In terms of quantum native applications, you have things like industrial chemistry, batteries, catalysts, fertilizers, polymers, superconductors. Really, really deep value pools and very difficult to solve problems. In that space, for example, we work with BP on homogeneous catalysis, which is considered one of the holy grails of combinatorial chemistry. Along those lines as well, in the quantum native camp, drug discovery, pharma biotech applications, enzyme design, biomolecular simulation. The work that we've done on inhibiting the KRAS mutation that causes 25% of all cancers and a larger number of the most dangerous cancers that we did with Dana-Farber is in that camp. There's also quantum mapped applications, things like optimization, simulation, machine learning, areas where there are, again, very deep value pools, networks, logistics, finance simulations, computational fluid dynamics. These are very difficult problems and problems where quantum provides an advantage, and they're high utility. Those are the attributes that we look for, and we've done work in those spaces with folks like BMW, BBVA, and BASF. I'll say that in terms of hardware-agnostic software providers, really, I believe that we stand alone in terms of the amount of work that we've done with really high-quality Fortune 500s and with the public sector, such as DARPA, and the foundational work that we've done including these areas here. One of the things that we're particularly proud of is the cancer research we did with Dana-Farber Cancer Institute at St. Jude Children's Res earch Hospital. That work was actually using quantum computers in a hybrid quantum classical device to be able to create new molecules to inhibit the KRAS mutation. The KRAS mutation, for those that aren't familiar, is the mutation that causes 25% of all cancers, a higher number for the most dangerous cancers, and for decades it was thought to be undruggable. It's a very important target in oncology. We worked with Dana-Farber, with St. Jude, with University of Toronto, and we came up with a solution that actually created 15 different molecules that were worth synthesizing outside the computer, and a couple that actually showed binding activity. This is really important because it is the first application, end-to-end, in the case of drug discovery. Nature Biotechnology obviously thought it was very important, and that's why they chose it as one of their top 10 papers of 2025. It solves for what we call the three V's. It is a validated application. We actually went end-to-end and produced new scientific knowledge on the basis of a quantum computing software. It is a valuable application. Obviously, it's something that is very useful in the world and has a great deal of utility, we think it's obviously just the tip of the iceberg when it comes to drug discovery applications. It is a viable application, meaning that we showed that quantum computers can actually add value. This was actually carried out on a 16-qubit quantum computer, using IBM hardware. So it's something that lays the foundation for future work in the biotech space and in quantum chemistry in general for Zapata going forward. The idea is that we need to do this for many other application areas. Unfortunately for us, however, this is very difficult work, and it requires a great deal of expertise. It requires a great deal of collaboration in order to actually drive to ground applications and create quantum applications using advanced tooling such as we do. The reason why this bottleneck exists, the reason why this takes a great deal of time, is that first of all, the landscape is very dynamic. If we move through a quantum solution for a classical problem, it ends up being something that takes a great deal of time, and by the time you come up with a solution, the space will have advanced in the meantime. It's a very dynamic landscape. Second, it requires a great deal of technical rigor. Many of the other folks that are attacking the problem, folks like consultancies, don't have the same degree of technical rigor. As I said, we were born out of a quantum computing lab at Harvard, so we have a deep commitment to technical rigor, and that's why we have 40+ significant peer-reviewed scientific papers. Third, you need objectivity. If you're working with one of the full stack providers, they have a vested interest in pursuing and maintaining an aggressive posture with respect to their development timelines, with us, hardware agnostic, we can be more objective. Finally, we add in-house expertise to those that don't have it. Folks like drug companies, finance companies, manufacturers, network companies, et cetera, that are looking to solve those problems. Really, these are some of the bottlenecks that we solve with our tooling. Another thing that I'm excited about is applying AI to the problem. As some of you may have seen, we actually announced a collaboration with NVIDIA in the quantum computing space in terms of applying agentic AI to quantum algorithm development. This is a very significant body of work. It's not a small number of scientists, just for a very short period of time. It's very significant, the work that we're doing. We've actually started to prove out an approach for quantum resource estimation, which is really an underappreciated bottleneck when it comes to quantum algorithm development. It's something that we arrived at as one of the key steps in the quantum algorithm and application development process. This was something that we worked on with DARPA over three years, with our work in the Landmark Quantum Benchmarking program. This is something that we think is going to be instrumental to driving quantum applications and algorithm development going forward. Our solution is based on our work with DARPA. As I said, we were the only company chosen for all three critical phases of the DARPA Landmark Quantum Benchmarking program. This really laid the foundation for a lot of modern quantum application development. It lays the foundation for the work that we do and the software that we have built, including QuantumGraph and QuantumPilot, which here I'll show you on the next slide, are our unified tooling layers for the full quantum application life cycle that moves us from discovery of applications to development of applications to finally deployment. There you see a couple of screenshots in the back there. That's QuantumGraph. That is really the ontology of applications and algorithms, that is then composed into applications in QuantumPilot. You see there a screenshot of QuantumPilot, and this is basically a more advanced tooling of all the work that we've done historically for folks like BP, BASF, Mitsubishi Chemical, BMW, and others. Our work is backed by foundational IP developed over 8+ years. We were really fortunate to have been very early in the space. They say timing is everything. I think that we were actually a little bit too early as it relates to quantum. We've been working on quantum software for eight years. The flip side of that is because we were very early, we now have 60+ patents that cover three strategic control points. W e actually had a major global consultancy come in and do a patent review. They found that our patents covered program compilation, optimization methods, and information retrieval. We are very aggressive with respect to intellectual property. One of our co-founders, Jonny Olson, is now VP of Strategy and Operations. He's actually the only quantum physicist/patent attorney that we're aware of, He has a unique knack for finding new IP and ensuring that our existing IP is well protected and continue to serve the delivery of our platform going forward. Our team, as I mentioned, is a team that has its roots at Harvard's Quantum Computing Lab. Myself, I spent a decade prior to Zapata growing a machine learning firm in the environmental commodity space from $5 million of revenue to $1 billion of revenue over 10 years. I have an MBA from Harvard Business School and an applied math degree from Harvard College. The rest of the team, our CTO, VP of Product, and VP of Operations and Strategy, all came from Harvard's Quantum Lab, as I said, and we have many more behind these folks that are serving to advance our purpose. Our model is analogous to other frontier tech models. For example, Palantir, we have a modular platform that captures value across the entire quantum application. The total addressable market that we're looking at is currently $2 billion and growing at a 35% CAGR, according to McKinsey. That's going to see dramatic growth if we get to anything like the $2 trillion of value creation that McKinsey estimates by 2035. We have validated commercial demand through enterprise engagements, many of the folks that I've talked about, government-funded research, multi-year programs at DARPA, and strategic partnerships with hardware folks. These are just examples of things that we've validated historically. We also believe that there's a great deal of upside potential from licensing our IP and moving on to strategic acquisitions to expand our capabilities and our value. Finally, I'd like to talk to you about why I believe this offers a very unique, attractive entry point into quantum. I think the graph on the right says it all. If you look at the market caps of other quantum companies, we are but a sliver. Right now, our market cap is, I believe, something like $200 million. The reason for that is we're at the very, very tail end of our restructuring. At this point, our restructuring is complete. We were, as many of you know, previously listed on NASDAQ. During the restructuring, we did move down to OTC, and we're at OTC. We are at OTCQB at this point. We do believe we're going to be moving up to NASDAQ and NYSE and are in the process there. But, because we're at the very tail end of the restructuring, we're very early in the journey in terms of our market cap, and certainly relative to other publicly traded quantum companies. During the restructuring, to be clear, we did clean up the balance sheet. Over $20 million of debt was restructured. Our operations have been fully restored. Key technical leadership is intact. We have paved the road to the higher valuations as the foundational quantum software company. In terms of significant potential near-term catalysts, obviously increasing investor awareness that we are through the restructuring and firing on all cylinders. The NASDAQ/NYSE uplistings, as I discussed, commercial conversions. We do have a healthy pipeline, including former customers that were very happy with us previously before the restructuring. Partnerships, I've talked about NVIDIA. There are other partnerships as well in the works, and government grants and programs. As I talked about initially, there are a great number of programs that the government is actually pursuing in a coordinated effort to sponsor and continue to promote development of quantum technologies, for example, the DOE Genesis Mission. We did apply to that program, I'll say our application to the DOE Genesis program included Lawrence Berkeley National Lab, MIT Lincoln Lab, as both subs, and a letter of support from NVIDIA. It was a very strong application, and we expect to continue to make other strong applications as well. At the same time, we're continuing to make scientific IP and product development progress. Excited to show you guys plenty more in terms of the direction that we're headed and the transformational work we're doing to capture the opportunity in quantum software. Finally, I'd like to leave you with an idea, which is that we are only at the beginning of what is going to be done in quantum software. If you look back at the other computing timelines, this is a quote from John von Neumann, who was a famous physicist at the time of the advent of the classical computer. What he said at that time is that the most important uses for classical computers are likely to be those that are most difficult to predict. We believe it will be the same with quantum computing. We are only at the beginning of arriving at which of these uses, the most important uses, are going to be transformative for quantum computing. The work that we do with partners commercially, public and private, is the work to discover those applications that are going to drive that transformative value creation from what we believe is the next tech super cycle and likely to be as big or bigger than anything that has existed before. We're excited to do it and excited to bring our investors along for that ride. With that, I am open for questions. I'm going to shoot. I have a question coming in from Tyler Anderson. Tyler asks, "How would you weigh the amount of effort that is spent between implementing algorithms and developing new ones?" It's a great question. I think that at the end of the day, you come back to the layer cake that we described, that I described, the why, the what, and the how. The what is the algorithm layer, and the how is the hardware, and the why is really the connection of the algorithms to applications. Really, I'll say that the most under-resourced part of that equation is connecting applications to algorithms. These are very, very difficult scientific problems, and they require a great deal of expertise across multiple domains in order to be able to connect an application or a problem instance to an algorithm. Our development of Quantum Graph, the ontology of those algorithms, is really a solution to that problem. For us, implementing new algorithms is definitely important. There are other parties that are doing that as well, and that's something that we've done historically in spades. The connecting of those algorithms to problem instances, I think is the most under-resourced challenge, and that's something that we focus on. Andrew Reese asks, "Zapata just closed an oversubscribed $15 million strategic financing. What key near-term milestones?" Definitely. First of all, I'd say commercial conversions. We're working on our commercial pipeline as we speak, and that includes not just private sector, but also public sector. We're applying to public sector grants, that's work that we've done historically. We were the key partner to DARPA. I expect that work to come back online as well. Commercial conversions, continued product developments, continued partnerships like what we had with NVIDIA, increased investor awareness, then obviously the up-listing. Lawrence Kenny asks, "The company strengthened product and go-to-market leadership with Romero and Rotondi-Gray. How quickly do you expect tho se additions to show up in pipeline growth and revenues?" We're working on it, we're seeing a great deal of engagement. Honestly, when I go to conferences, when we spoke at Davos, when we were recently at QTech, there's an extraordinary amount of engagement, both from customers that we had historically and new customers. I'm not going to provide any guidance here right now specifically, there is an extraordinary amount of engagement. S. Venuti asks, "Are you seeing interest from regulated or defense-related sectors? If so, how might that influence revenue visibility and contract length?" We are certainly seeing interest from regulated and defense-related sectors. I think that from the executive branch on down, all sectors of the government are sponsoring further growth in Quantum. We are very much in line to receive that work. We will be working on that. It will create greater visibility and contract length because many of these contracts have long durations. All right. From Jonathan Berry, "Your cancer research was named a top 10 paper by Nature Biotech. How are you converti ng that scientific edge into paid pharma and biotech engagements right now?" Literally, that's the work that we're doing, I'll tell you that how we're doing it is really using that as a template for other work. The good news is that we have a number of templates. We have homogeneous catalysis that we did with BP. We have computational fluid dynamics that we have done with plenty of other folks. We have manufacturing with BMW, we have the biotech cancer research that we did with Dana-Farber and St. Jude's. Really, what we're doing is using that as a template for the tooling that we are creating with Quantum Graph and Quantum Pilot to make that work that ultimately took years in each one of those cases, contract and compress that timeframe down to months. Really it's serving a critical purpose as a template that other people don't have. Eric Walters asks, "Can you share about how the NVIDIA partn ership on agentic AI could accelerate commercial adoption of your quantum algorithms, what success would look like over the next 12 to 24 months?" This one is really important for us. We saw firsthand that the quantum resource estimation phase of the application development process was very arduous. It often took, as you noted, 12 to 24 months with teams of PhDs across multiple domains. What we think success looks like is to compress that process that previously took a year to two years and often a dozen PhDs, compress that process into something that could take initially months, then ultimately weeks and even days, and require a much, much smaller team. Still a great deal of technical talent, a much, much smaller team. I do think that over the next 12 to 24 months, both NVIDIA and Zapata are focused on ensuring that that partnership is going to result in a compression of something like two years to a number of weeks. All right. Lots of questions. Really appreciate that. It's time to wrap up. Is it? Okay. Great. We'll wrap it up. Again, I'm very appreciative of all of you joining. We are as excited as ever to pursue our mission in quantum software, and I think that with what's happening in the industry, we couldn't have picked a better time to really have gone through the restructuring and pursue the next phase of growth for Zapata. We'd love to have you along for the ride.