In The Arena by TechArena - Q-CTRL on Quantum Computing’s Path to Production

Episode Date: August 12, 2026

Quantum computing is moving out of the lab and into real production environments. In this episode of Data Insights, recorded live at Xcelerated Compute in New York, Allyson Klein and Jeniece Wnorowski sit down with Alex Shih, VP of Product at Q-CTRL, to unpack what that transition actually looks like.

Welcome to Tech Arena, featuring authentic discussions between tech's leading innovators and our host, Allison Klein. Now, let's step into the arena. Welcome in the arena. My name's Allison Klein. We are coming to you from the Accelerated Compute Conference in New York. And this is a Data Insights episode. That means Janice Norowski is with me. Hey, Janice, how you doing? Hi, Allison. Great. How are you? I'm fantastic. And I am so excited to be on the East Coast. with you. Normally we're on the West Coast. So this is just, it's game-changing. We also have a game-changing interview series this week. So why don't you tell me who you brought with you for this first interview? Yeah, our very first interview is really excited to kind of kick things off. Everyone's

talking NeoCloud and I factory, but we're actually going to talk some quantum computing. So today I have Alex. Alex, Alex, welcome to the program, Alex She, who is the VP of Q-Control. And it's so nice to having here. Thank you. Thank you for having me. It's an honor to be here. And as you mentioned, I'm the VP of Product at Q-Control and we're excited to just talk and share a little bit more. So Alex, Q-control first time on the show. So why don't you just start with an introduction and what your role represents at the company? Sure, yeah. So as I mentioned, my role is a VP of product at Q-control. And fundamentally, what I do in my role and what our team does is really transform the deep science, the quantum physics and quantum control expertise that we develop.

into usable and scalable solutions that customers can use. And these customers are data centers, HPC, high-performance computing facilities, as well as enterprise businesses. We also build ed tech solutions, so even universities, as well as businesses that are trying to upskill their workforce, deploy our education platform into their systems

to really grow that workforce talent around quantum. We also build quantum assure navigation solutions. So we take the same kind of quantum control software and deploy it onto commercial aircraft, defense platforms, to provide GPS backup navigation. So what we do is we take all that expertise and deploy it into production-ready, enterprise-grade solutions. And beyond that, I should also mention that we take these solutions,

we build these products, but our team explores how do we deploy them into integrated holistic systems? And that's why we are at this event, Accelerated Comput, because we're talking to all these data centers, all these facilities, all these component vendors to explore. What do we need to do? How can we work together to bring our software into these production environments? So it's not every day you just run into people in the quantum computing space. right? But tell us a little bit about how you specifically got into this career and how did you build this and move into the quantum industry. And what really drew you to this and set yourself apart from

going somewhere else? Yeah, it's a great question. I live in San Francisco and have spent a majority of my career working at big tech, small startups, primarily in the deep tech space. So working across big data companies, enterprise solutions, but particularly space tech solutions as well. So I've always been fascinated with building solutions that are really trying to go from zero to one or really zero to point five in pioneering these playbooks. And I most recently came from Slack. And yeah, and I was building a lot of our software products. Mostly during COVID, we are pioneering new innovative surfaces to help teams, help people connect when everyone was still globally distributed and quite remote.

So it was a fundamentally new paradigm and problem that we were trying to solve, and that was quite exciting. I did that for a couple of years and then got connected, and I was aware of quantum and what was happening, but a lot of it was coming from just more of the mainstream media. And I got connected with Q control, learned a little bit more about the problems that they were solving, but more importantly, the opportunities they were pursuing.

And I've always viewed quantum technology as a generational step change technology that would really change our introduce a new compute paradigms. And so the opportunity to work on something like that, especially for the next generation and thinking about how my kids are going to be encountering this technology, if this industry is successful, this would just be ubiquitous and it would just be in the background, for every kind of computational execution and operation they use, it's quite exciting to be at the front row of working on a technology that's really going to impact this next generation.

But even with that, I was really drawn to the specific use cases and applications that this kind of technology can potentially unlock. Things like optimizing transit networks to expediting and accelerating the whole drug discovery simulation process, especially when it comes to AI and machine learning, really accelerating some of the model development in data generation process. And so all of those kinds of potential use cases, this horizontal paradigm shifting to computational technology was quite interesting. That's fascinating. Now, we know that in quantum systems today, the reliability and ability to sustain a quantum computer

is one of the biggest challenges. And one of the things that I've read about your software is that you're trying to address instability in real time with your software. How does that work? And how do you translate that into something that you work with customers on? As an overview, Q-control develops AI-powered,

error reduction quantum infrastructure software. And what we tackle is one of the core problems that this industry continues to face, which is hardware instability, noise from all sources within the hardware as well as environmental noise, as well of the ability to scale that hardware. While you may see a lot of development and achievement, these problems still are huge barriers to this industry from scaling. So we develop that software that helps the hardware teams to accelerate and scale the development

of their cubits, of their architectures, to, enterprises who are trying to experiment and test out algorithms and applications on real quantum hardware, and we provide the stability when we reduce the noise so that they actually get reliable, accurate output as a result. And they can actually properly and accurately assess how their algorithms are performing on a real quantum computer. So a lot of today's progress in quantum computing is focused on improving the underlying physical systems and addressing the challenges like stability and error rates. Where do you see the software control systems unlocking in the next phase of the industry?

So I'd say, yeah, so that's where we really fundamentally started. And we were doing it. We view ourselves as we started off as a picks and shovels provider, which allowed us to develop these targeted point solutions for very, very specific problems, such as, yeah, hardware instability, error reduction, error suppression for specific circuits and algorithms that are submitted to real hardware. So our products address those parts of the stack. In terms of where we're evolving, is what I see what's happening with a lot of the industries, especially as we're starting to integrate into classical compute clusters with GPUs and CPUs in HPC-type facilities,

I see there's a few different approaches. One is providing what we call quantum containers. So the software that really brings together the hardware, as well as the control lot electronics, and the fridges that are necessary, and all the other components providing a ready-to-go system that can be easily deployed into a data center or a quantum compute center, and it just works.

It all plugs in. We run our software and it just works, which is a huge undertaking today. You need teams of PhDs today to both get the QPU and the processor set up. You need other teams to help connect them to the electronics. You need teams that integrate everything together. So we provide software that just works. And we just proved it just last week.

We went live in Denver with Elevate Quantum. And it was a record in terms of from concept of an announcement of a system that was going to be deployed to actually going live, it took only five months at a fraction of a cost that it would typically take to get a end-to-end quantum computer up and running. So that's a huge area. Congratulations. Thank you. Thank you. Yeah.

Congratulations to our partners and especially to elevate quantum as well. But that's really where we see our solutions as well as the industry moving is how do we accelerate the way that we deploy, the models that we deploy into real world platforms, not just these experimental test beds just for quantum systems, but how do we integrate with the clusters that organizations are already using? And you can imagine when we start to deploy there, there's all these other software layers. and integration points that we are interacting with. So those are all areas of expansion for our product portfolio. That's a huge milestone. And I think that one of the things that Janice and I have talked about quite a bit is that it feels like the momentum behind quantum is accelerating.

And we're moving from these conversations of early stages and in labs to, hey, when is this actually going to start hit that pivot point where it's becoming deployable as products and utilized? And I guess my question for you, since you're so deep in this, is where are we on that arc? And what would you want to say between the lab and production deployments? Yeah, yeah. How are you approaching that? I mean, that strikes the heart of what we do as a company, which is exactly to bring our solutions into business environments, into production, enterprise applications, and into real-world environments.

We are not in the business of just publishing papers and providing barriers or inaccessibility to actually use our tools. We want our tools to be accessible, to be used by companies all over the world. We're starting to see significant milestones coming up. And credit to a lot of the quantum computing hardware vendors who have been very transparent about their roadmaps. and some companies are actually hitting the milestones in their hardware development. Some are evolving and they adjust, which is natural, but we're starting to see that transparency. And our software, because we are hardware and vendor agnostic, we are quite flexible and working with different vendors depending on their readiness, depending on their traction,

and who they're targeting. And so we go at the pace of however fast, a lot of the hardware vendors can move. And so in terms of like where we see what's happening, like I mentioned with elevate quantum, this is by many regards the fastest deployment of an intuant system. And I think it really now has proven just how much we can shorten that entire cycle. And I suspect that the next deployment is going to be even faster because we have essentially created that that blueprint for how this works. We've seen Murphy's Law at play. We've seen everything that can go wrong, did go wrong, but now we have ways to mitigate for that. And so the next deployments are going to be much faster. And so I'd say even though we're at the beginning stages, as data centers are

more receptive to this, we could deploy more, but they are obviously motivated by the business ecosystem. So we are also in the business of working with the industry to motivate and incentivize the business ecosystem to want to execute more, more applications, more workloads through these different compute clusters. Nice. So in your opinion, how important is developer experience, right? And with that developer experience, how do you see that moving things to go more mainstream for Quito? Yeah, that's a great question. We started off focusing on developers, specifically quantum algorithm developers for our product FireOPAL, which focuses on the error reduction pipeline for any kind of algorithmic execution.

We were targeting quantum algorithm developers to start. Since then, we built what we call helper functions or layers of abstraction on top of just the algorithm input. So now what we can do is expand the types of personas we work with. And these personas are more domain experts, maybe like the chemistry analysts or the applied engineer or applied researcher or the optimization expert who can now bring the data formats, the cost functions, the network graphs that they're used to in their own software and submit it to our tools to execute. And so we provide these layers of abstraction. So now that's another persona that we target. But in addition to that, now the other persona we've unlocked is also application developers.

So we provide a fundamental pipeline, a core pipeline that just optimizes and improves any kind of algorithmic execution. And while we've built some functions on top of that, other applications, developers, such as fluid dynamics experts or potentially other quantum chemistry companies who have their own algorithms and their own applications, they have the ability to actually build and integrate on top of our core pipeline to actually have a boosted performance for what they've already developed. And we've already proven it with a couple companies we work with, Calibri TD, based in France and Canova Computing based in South Korea. They target their own specific industries, but they've actually integrated and tested out their

applications on top of our core pipeline and have shown improved performance from what they could deliver before. So that's, you know, some of the additional personas as well as other kinds of developers we've unlocked. But I'd also say with our ad tech solution, we target a lot of just traditional software developers as well who are interested in quantum. And already in many ways have the software computer science expertise to work with it. And so that's another developer persona that we've been targeting from the beginning

and it's been our fastest growing product ever since its origin. And so we've seen cases where you'll have a computer science engineer or a platform developer who cares about quantum and just learns a lot of the core concepts and is immediately able to just start programming and building out algorithms that can be executed on a real quantum computer. That's awesome. Now, I was doing some research in advance of this interview, and you talk about quantum computing, but you also talk about something called quantum sensing. Can you introduce this term and tell me exactly what it is and what it's for? So there are many applications for quantum sensing from medical imaging to navigation.

The problem in the opportunity that we're really addressing is quantum assured navigation. And what I mean by that is we provide a reliable backup to GPS navigation. And to underscore how big this problem is that by many accounts, GPS denial or GPS reliability or unreliability accounts for over one, billion in economic value per day. And especially in today's geopolitical situation, GPS is often mission critical. It is often the only source that critical operations rely on for navigation, positioning, and timing. And so what we provide is a reliable way for mission critical platforms or even commercial platforms to have a reliable backup to GPS.

So let's talk a little bit about what happens when organizations start to experiment with quantum computing. What challenges really surprise them? Can you comment on things like capabilities or just overall practical challenges? So there's a couple responses that come to mind. one is when they try to execute some application or algorithm, the first time it often may not work. And that could be due to a number of reasons. One is, again, because of the inherent noise and error that exists today. And that's why our solution is so critical.

Other experiences are that the standards are just non-existent. And so there's just a complete lack of interoperability across all the things. component. So things may fail across the execution chain. And so there's a lot of troubleshooting involved. But again, that is where our solutions have really expanded and evolved. Just by being that software layer, we provide a lot of that interoperability. And then there's oftentimes long queue times as well and questions about status. And we've actually taken upon ourselves to provide updates and insights, even in some of the in-client messages about what the status is, where it is along the execution path to provide a little bit more transparency.

So you can imagine for any new technology, there's all these kinds of just fundamental vendor-to-vender-related issues that do come up. But that is where we have been at the forefront of developing solutions that really provides the glue to make it all work. But the other response we get oftentimes, which really delights me, is when customers, especially ones that are using our products exhaustively, find that they are able to unlock new results that they had not achieved before. And we see this over and over again. Just in the past couple of weeks, we've seen new publications, new papers by our customers about new results that they

have achieved, that they just were not able to do so with any kind of classical state-of-the-art solution. And that is really exciting. And oftentimes, they'll send us an email or they'll have a LinkedIn post where they will explicitly say that they were surprised, that they could even achieved this scale or this scope of a problem. And it's all thanks to a lot of their domain expertise, as well as the hardware partners we work with, and them just having confidence, the customers having confidence in faith in using our software to try to push the boundaries of what area of research or application they're looking into. Now I'm going to ask you the million dollar question, which is, I think that Quadsum has been focused on some of the

scientific research areas and very technical computing types of challenges. If you look out ahead at the long-term roadmap, do you see this also integrating in with mainstream computing stacks? And where do you think the use cases are going to flow in that space? Yeah, I 100% believe that this will integrate into enterprise workflows and production platforms and production technology stacks. And that is something, again, as I mentioned, that we are actively working in. We're still a bit of ways away, but we are uncovering and unlocking

every step that's going to allow us and allow the ecosystem of partners we work with to integrate into these kinds of production environments. And again, what we've done with elevate quantum, I should also mention that we've integrated with Rican in Japan, which is another global lab that has the world's fastest supercomputer, Fugaku.

Yep. And also has the IBM system to quantum machine on site as well. And so they are actively exploring how to integrate quantum compute with classical compute for real world hybrid workflows. And so I do believe that we are pioneering how these kinds of integrations will look. And then in terms of use cases, I think there is a. a number of different kinds of use cases and applications you may hear about. We have extensive experience in optimization, so around logistics, transit networks.

And we are fairly confident that given the roadmaps of the hardware vendors that we've seen, if they continue to deliver on the milestones of their hardware, we have confidence that in the next two to three years, we will see meaningful quantum advantage in specific workflows, especially around optimization. And again, that's based off of just the historical experience and trends that we have seen working with customers

on their specific problems. So we have confidence in that timeframe for some meaningful advantage as well as integration into some kind of commercial workflows. But in a difference, to that, areas around quantum simulation, which has a variety of applications from next generation electric vehicle and battery development to solar cell development, to drug discovery, we have confidence that there are signals of advantage even there. It'll be a while before it starts to

reach commercial advantage and commercial impact, but we think that there are signals of at least some meaningful scientific demonstrations coming soon as well. And then there's exploration around quantum machine learning. And while we leave it to customers and users to really experiment with that and using our software to help, there was one organization, Mazda, that we worked with, and we published this as a case study, where they were looking at the most efficient vehicle frame design for ongoing R&D at their own facility. And they used a lot of training data

to constantly optimize the structural design of their vehicles. And we looked at a problem that they had optimized previously using best-in-class state-of-the-art tools to do so. With our software on a real quantum computer, they found that it required five times less training data. to come up with the same design. And so that's just another area that's quite exciting, where in that particular case,

it has a direct translation and implication of reduced R&D cost. If you require less data to come up with the previous best-in-class designs, that there is some signals, some promise in those kinds of applications as well. Very cool. Amazing, Alex. I think our listeners are going to want to learn more and hear more, So where do they go to contact you and your team and to get more information? Yeah, it's a great question.

You can always find us at our website, which is Q-C-T-R-L.com, and keep track of the many updates that we often post in our blog, as well as on social. So on LinkedIn, you can look up Q-control as well. You can always connect with me. I'm on LinkedIn. My name is Alex Shee, and so you can always reach out to me as well. Alex, thanks so much for being on the show. That wraps another episode of Data Insights,

and Janice, thanks so much for being here with us. It was a great episode. Thank you. It was awesome. Thank you. Thanks for joining Tech Arena. Subscribe and engage at our website, Techorina.ai. All content is copyright by Tech Arena.