Sebastian Hassinger (00:01.321) Daniel, thank you for joining me. It's great to have you on the show. I think you're the first computational, electromagnetic computational researcher that I've had on the show. I'd really like to get a little bit of backstory for how you sort of found your way into quantum computing from an electrical engineering sort of starting point. Daniel Faircloth (00:24.211) Yeah, sure. Thanks for having me. I'm truly humbled to be here. Yeah, I think the story, you know, I was an electrical engineer by training, you know, in grad school, I was working on the combination of computational electromagnetics and novel optimization techniques, basically to do interesting device design, you know, things that Sebastian Hassinger (00:28.884) Hahaha. Daniel Faircloth (00:53.646) You can go to a textbook, you can see, here's kind of a basic antenna or what have you. And you can kind of see, this is how you would parameterize it if I wanted to do a basic optimization. And we were trying to really sort of break that mold and say, well, if you give powerful electromagnetic simulation tools and powerful optimization tools to an already well-versed and trained electromagnetics engineer, whatever their interests may be, can you effectively turn them into a super engineer and unlock creativity as far as what they're able to optimize? And so in grad school, was working on these types of problems and I kind of have a distinct memory of sitting at my desk and thinking, man, it would be so awesome one day to start a company that you know, basically does this. And, of course, at the time I at least knew that I didn't know anything and I, didn't know enough to, to embark on that journey. So, you know, if you're in the U S and doing graduate work in electromagnetics, you're probably going to go work in the aerospace and defense industry. And so that's, that's where I started and, you know, worked at a great, great, right. Yeah. Sebastian Hassinger (01:54.165) Hmm. Sebastian Hassinger (02:14.143) Right. Sebastian Hassinger (02:18.035) That or telecom, right? mean, you didn't get swept up in the 5G deployment, I guess. Daniel Faircloth (02:22.814) Right. Yeah. You know, got moved into the defense industry and worked for a great company and eventually, you know, wanted to tackle complex electromagnetic R &D problems, wanted to see that R &D turn into fielded systems, actually make it through the valley of death and turn into real world things. And so I went to work at Georgia Tech Research Institute, GTRI, started there in an antenna group, was doing a lot of computational electromagnetics and design work there. Interestingly, that group, that lab that I worked in also had a quantum group that was in the early days of working on ion trap designs. And I got to working with those people and was looking at the way that they were modeling the design of ion traps and observed all the limitations that they were struggling with. Sebastian Hassinger (03:36.841) Mm-hmm. Daniel Faircloth (03:37.454) the so-called gapless approximation where you're taking an analytic formula and you just sort of assume the world is an infinite metal sheet and you kind of trace polygons in that. And of course that is okay for roughing out an initial design, but it doesn't hold water for too long. And the other tools that were available at the time were just really severely limited. Sebastian Hassinger (03:56.532) Hmm. Daniel Faircloth (04:06.112) And so I kind of just thought I can maybe write a little prototype code and see if it helps this group move forward. And next thing you know, that prototype code is snowballing into this whole effort to build a true ion trap design software package that we were using internally. you know, I've had the opportunity to work with some Sebastian Hassinger (04:28.031) Hmm. Daniel Faircloth (04:35.512) great folks that helped. wasn't a computational scientist or a quantum scientist by any stretch. I'm just an electrical engineer. But I knew how to write modeling software. And so they were teaching me things about what's important about the ion trap world and the things that they care about. Sebastian Hassinger (04:40.798) Right. Sebastian Hassinger (04:47.316) Right. Sebastian Hassinger (04:55.733) And those, the shortcomings of the existing methods for simulation or optimization, did that remind you of the sort of the, what you were finding in your graduate work sort of of like the question you asked about if you gave power, more powerful, more accurate tools to an electrical engineer, would you turn them into sort of a superhero engineer? Was that sort of the same kind of whatever? cutting corners or over approximation that you were finding in the current, the existing ion trap tools. Daniel Faircloth (05:29.548) Yeah, to a large extent, that was the case. were, there was essentially no optimization capability that existed. If anybody was doing optimization, was, you know, pretty local optimizers, pretty basic stuff. And then, you know, on the physics modeling side of things, had, you know, kind of gross approximations and then tools that weren't designed for ion trap design by any stretch. And so we're kind of, you know, the community is sort of trying to shoehorn the ion trap design problem into these tools that weren't built for that. And to get to the point where you can design Sebastian Hassinger (06:12.629) Mm. Daniel Faircloth (06:20.322) the architecture, the layout, and be able to produce reliable control voltage solutions so that when you go into the lab and you're dealing with limited resources in the lab, you have a really great starting point from the simulation. Essentially, none of that existed or it existed in such a severely limited fashion from an accuracy and scale standpoint. Sebastian Hassinger (06:35.892) Right. Sebastian Hassinger (06:47.957) Was that a, was that, yeah, I was gonna say, was that a matter of the two of the existing tools were more around antenna design for, you know, a telecom or aerospace kind of application. This was literally trying to hold individual atoms in a field and manipulate. Was it just a matter of the extreme differences scale? Daniel Faircloth (07:09.134) In a way, I mean, there were some tools that were built for electrostatic simulation, but they weren't, you know, I think to be fair and from those tools designers standpoint, you know, the origin of those tools, they weren't thinking about ion traps and the subtleties that are involved there. How do you predict the ion height to so many nanometers? Sebastian Hassinger (07:31.145) Right. Right. Daniel Faircloth (07:38.612) and how do you create shuttling voltage solutions to so many millivolts of precision. There's just a different class of size scale, different physics are involved. Sebastian Hassinger (07:45.011) Right. Sebastian Hassinger (07:53.341) And so did you write some of these simulation optimization tools as part of the paper that you co-authored in 2013 in the new Journal of Physics, sort of demonstrating that it was transport through a micro-fab x-junction trap, I think, at GTRI, right? Daniel Faircloth (08:10.144) Right. Yeah. Yeah. Those tools eventually led to, to that work. You know, one of the, at that time, one of the big problems was this whole idea of how, how are we going to turn a corner? You know, an ion is going to have to turn a corner. If you think of an ion trap, this is my electrical engineer gross over approximation. But if you think of an ion trap as a, as a fancy train track system or a, you know, city streets, Sebastian Hassinger (08:27.551) Mmm. Sebastian Hassinger (08:34.538) Hehehe. Daniel Faircloth (08:39.874) and the ions are being shuttled around, you need to be able to turn left and turn right as you grow and scale. And it was just sort of that simple sounding problem that was very difficult at that time. And how do you get the, one of the problems was how do you get the ion to turn but not get heated in that process and you lose the ion. Sebastian Hassinger (09:06.51) Mm. Right. Daniel Faircloth (09:08.398) too much energy is imparted to the ion and it goes flying out of the trap or something like that. Sebastian Hassinger (09:11.603) Right. Right. I'm getting an image of a matchbox car going down a plastic track and flipping over. Yeah. Yeah. Daniel Faircloth (09:19.95) Exactly. That's the exact image in my mind. You're in a race car and you sort of lose control and you go flying off. And there were some papers leading up to that that had these heating equations in them. And I just kind of naively thought, well, what if we use some of this geometry and topology optimization stuff that I'd worked on before? and say, and map it to the cost function of ion heating. And so the name of the game then becomes, it's kind of a two stage optimization. One is, can we design the geometry of the trap to put you in a favorable position for being able to then optimize control voltages to turn a corner. And, you know, of course, right. And, you know, there were several other Sebastian Hassinger (10:11.827) How much of a bank do you need on the track? Daniel Faircloth (10:19.54) engineers and scientists, you know, that were contributing to this problem. I, you know, I've just you know, part of the fun in designing this optimization process. And, and, you know, it was successful, you know, that, that translated into the lab. And of course, there's always discrepancies between simulation and, and, and the real world. And so the, the guys in the, in the lab, that team worked to take that sort of initial simulated solution. we got the trap built. Sebastian Hassinger (10:28.81) Yeah. Daniel Faircloth (10:55.756) And then they had to do some tweaking on all of the control voltage solutions. And then we were able to demonstrate turning the corner. Sebastian Hassinger (11:06.229) That's really cool. just to frame this, that X track, that was sort of an early attempt to break the scaling challenge of ion traps where before it was just a one-dimensional trap and there were just physical limits. You couldn't really get more than I think 30 roughly ions in a single 1D trap. So the idea was to sort of cross two single-dimensional traps into this X and then be able to, as he said, of shuttle the ions into one of the side tracks as you move things back and forth. Just to increase the overall capacity, guess, and also just connectivity, right? To sort of increasing the capability of the overall machine. Daniel Faircloth (11:52.256) Right, if you think of the estimates, even today, where we as a community are still chasing this problem, although obviously in 15, 16 years we've made massive strides, but if you just think of the algorithms that are theorized to provide practical value in the world, and then that gets mapped to, okay, given Sebastian Hassinger (12:00.969) Mm-hmm. Daniel Faircloth (12:20.322) heating rates and error control algorithms that have to be implemented. then, you know, you're ultimately mapping that to, how many ions do I need in a practical trap to then translate to implementing these real world algorithms. it's a lot, yeah. You know, just to put it mildly, it's a lot of ions. Sebastian Hassinger (12:35.689) Right. It's a lot. Daniel Faircloth (12:44.3) And the community's kind of doing battle of like, many ions do I need given these heating rates and these error correction schemes and all of that. And ultimately, from my perspective, if I sort of view myself as I'm creating a tool that allows the city planner, so to speak, to lay out the city plan for how this is going to look, how this architecture is going to look. Sebastian Hassinger (12:44.337) and there and Sebastian Hassinger (12:49.927) Right. Right. Sebastian Hassinger (13:03.764) Right. Daniel Faircloth (13:11.106) being able to give them a tool that is very accurate, coupled with optimization strategies for being able to design these things. just makes that job easier. Let's let's these designers scale up. Sebastian Hassinger (13:21.962) right. So take me from that 2013 paper and the tools and the software that you wrote as a part of that effort to actually the product or productizing that code. You did that in another company, right? And then you spun out Nullspace, is that right? Daniel Faircloth (13:43.63) That's right. Yeah. So I co-founded a defense contractor called Iris Technologies. And at Iris, were, as you know, that still are designing next generation RF devices for aerospace and defense industry. And, you know, just broadly speaking in terms of the scale of simulation, the accuracy requirements that exist to be able to design next generation RF systems. All of those problems, when you kind of describe it at that level, all those problems exist in the RF world from a simulation tool perspective as well. in the early days of Iris, I had departed the quantum computing world for a while. And in the early days of Iris, we started tackling these RF design problems. we're working on the technology that ultimately would spin out as null space. And it was, it was RF focused, you know, we're working on radar cross-section, we're working on antenna design, et cetera. And then, you know, an old friend from GTRI who had had since moved on, Curtis Mullen, who's a co-author on that cross junction paper, called me up and said, Hey, you know, I, do you, are you still working on these kind of tools? Like, there needs to be a tool like this. And he kind of really spearheaded the revitalization of that. And we had been developing technology for the RF side of things that would translate very well back onto the quantum side of things. So we took advantage of things we were already developing. Sebastian Hassinger (15:29.482) Hmm. Daniel Faircloth (15:40.232) and created what is now NullSpace ES. And we incubated that through Iris for several years. And then about three years ago, both our product and our ES product had reached a level of maturity where we said, we should spend this out as a commercial company and let the world have access. Sebastian Hassinger (16:06.035) Yeah. And I suppose the market conditions of quantum computing also would be a signal that it was time to spin it out. the thing I've been, the reason I wanted to have you on is that I've been really interested. I just had, is our metal see from quantum elements on a couple episodes ago, they're doing, know, sort of they describe as digital twins of mostly superconducting qubits. think there's application other modalities as well, but It's similar sort of recognition that lab space and resources and time and people are scarce and should be used in very careful ways. as much, simulation can play an enormous role in making the most of that, of those lab resources, those real world resources. And frankly, as soon as I saw this sort of category emerging, there's a couple other companies that I think are in this general group. it felt like a natural extension of EDA, right? In semiconductor design, there's all kinds of simulation and design tools that are absolutely, you can't build a 23 billion transistor chip without these tools. And it feels like we're going to go through this evolution of sort of adapting to the specific requirements of quantum computing. Daniel Faircloth (17:22.744) Mm-hmm. Sebastian Hassinger (17:34.645) in the same way. We'll build the same sort of tool chain for the modalities that turn out, know, that are around today or exist in the future or end up being the ones that hit those commercial values. So was that part of your buddy's sort of pitch to you is that this is going to be needed as the industry sort of matures and becomes more of a commercially, you know, productive kind of environment? Daniel Faircloth (18:04.718) Yeah, he was working on problems, solutions to problems to achieve, you really the first commercially viable ion trap based quantum computers. And then just over the evolution of six or seven years that have passed by since that all got started. I mean, you see how many companies have spun out, how many quantum research groups there are that are Sebastian Hassinger (18:17.717) Hmm. Sebastian Hassinger (18:30.997) There are 100 hardware vendors now roughly, give or take. Daniel Faircloth (18:35.084) Yeah, it's been amazing to see how it's evolved and all the different modalities. And so it's interesting, you know, they have the different modalities that are competing and then within that they're, you know, in, number of companies that are competing, given an architectural approach. Sebastian Hassinger (18:40.479) Yeah. Sebastian Hassinger (18:53.897) Yeah. Yeah. And you mentioned sort of the, you know, the productization, the maturity of the technology within Iris before the spin-out. I was reading up on your 2025 R1 software release and there's sort of a five times memory reduction. That's really impressive. I'm assuming that means the simulation requires five times less memory. Is that right? Daniel Faircloth (18:54.274) So, yeah. Daniel Faircloth (19:14.798) Mm-hmm. Daniel Faircloth (19:23.086) Right. Yeah, we, from my perspective, we have a solver that provides sufficient accuracy for a real industrial grade solution to ion trap design. However, you know, all the hunger of quantum scientists to be able to design larger and larger architectures is unbounded. So given that constraint, Sebastian Hassinger (19:24.66) Yeah. Daniel Faircloth (19:52.192) we needed to improve the efficiency of the solver while maintaining or even improving the accuracy. And so we leveraged our existing compression solver, which is really kind of the key to our success here, and then added high order basis functions and got those high order basis functions to work in concert with the compression solver. So you're kind of getting the best of both worlds. Ultimately, that led to the memory reduction and a speed improvement to allow simulation of larger and larger traps, which our customers are doing. Sebastian Hassinger (20:34.067) Right. Yeah, yeah. And I guess there's, you mentioned sort of the amount of effort and different sort of innovative approaches to getting scale and performance out of ion traps. Is that sort of the main way in which your, the customer requirements you're trying to fulfill are the ability to simulate larger and larger systems? Daniel Faircloth (21:02.326) Ultimately, yes, the first challenge we had to tackle was the accuracy requirement. They need to be able to rely on the potentials and fields that we're outputting to be able to design their motion control solutions, ultimately. And once we had a good handle on that, it became a matter of scaling. the idea is scale. ever and ever larger, or in some cases be able to produce sub modules, so more specialized solvers that maybe handle some early stage design aspects of the problem better. you know, if you think of the design challenges, I may ultimately need to design a very large trap. I mean, that's all going to be the case, but I got to start with some module level. simulation, but those module level simulations themselves are becoming more and more complex. So how do I enable more sophisticated designs, both at the small scale and the large scale simultaneously? So these are all challenges that we're tackling as we move forward. Sebastian Hassinger (22:22.259) Yeah, and I mean, it's also, it seems to me like it's a complex sort of marketplace from the perspective of you're enabling your infrastructure, right? Your design assistance, your simulation, your customer base are competitors with one another. And you like you had a webinar recently with Oxford Ionics Curtis Volin demonstrating null space. How do you... Daniel Faircloth (22:41.23) Mm-hmm. Sebastian Hassinger (22:51.487) How do you manage the fact that all your customers are potentially competitors with one another in that shared tooling, but disparate commercial goals and even architectures? Daniel Faircloth (23:11.222) Right. You know, we faced this on the RF side too with our null space solvers. In some sense, both solvers, you know, face the same challenge of we're enabling competitors to compete with each other more effectively. And one of the perspectives on that I would say is that, you know, Sebastian Hassinger (23:22.762) Hmm. Sebastian Hassinger (23:29.333) hehe Daniel Faircloth (23:39.288) from a tooling perspective, a rising tide floats all boats. So the better we can make the tool, these companies can do more. Maybe they'll come up with new and innovative solutions that differentiate themselves from one another. And one will achieve some competitive advantage in some area and the world will benefit from that. And I think that's ultimately what we're trying to enable is to allow more sophisticated designs to achieve practical value and let everybody have a shot at it. Sebastian Hassinger (24:11.017) Right. Well, and I suppose the alternative in those kinds of scenarios is for somebody to roll their own, grab the Python SDKs and libraries and whatever else and build something that's their own internally, and they risk not having best in class because you're focusing solely on this simulation and solvers and optimizers. And therefore, you're putting more sole focus on that than trying to build an entire fully integrated command computer. So I mean, I definitely see the risk of not using your tools if your competitors are, right? I'm really curious, you're in a position where you're coming out of a defense contractor and the tools that you're building are still potentially seen as dual use. Is that advantageous or is that complex or is it? Is it, are there advantages and disadvantages to that kind of dual use categorization as a technology vendor? Daniel Faircloth (25:15.512) There's definitely pros and cons. We want to enable our customers to have the best technology possible. And these different markets at different times in their evolution offer different levels of challenge. used to be, I think the old thinking was that the in the U S at least the defense class of problem or, or the space problem, you know, NASA and thinking all the way back to space race, that the government was leading the charge on all of the best technology development. And I think there are certainly still places where that applies, but we also see where the commercial world is leading and, essentially. Sebastian Hassinger (25:58.357) Hmm. Daniel Faircloth (26:12.556) The defense market contributes to the commercial world and vice versa. And as a tool developer, having our feet in as many different markets as possible, think ultimately serves to our advantage in being able to learn lessons in different places to push forward the technology for better, faster, more accurate simulation. Sebastian Hassinger (26:37.555) Right, right, yeah. And so you've come out of the ion trapping world and the product is still sort of bread and butter as ion traps. But I mean, I know for example, there's an open source electromagnetic simulation that the Amazon Web Services, the Center for Quantum Computing group under Oscar Painter put out called I think PALIS that's for electromagnetic simulations as pertains to superconducting qubits. Do you see other modalities as sort of adjacent market opportunities for sort product development in the future? Daniel Faircloth (27:13.804) Definitely. we also, I mean, just back to your point about why doesn't a quantum computing company in-house the development of their own solver? And we have a similar problem of, you know, we've got the same opportunity or a similar set of opportunity costs in front of us and we have to weigh where we spend our time investment. And the ion trap architecture design problem that NullSpace ES solves is certainly not the only tool in the tool chain, even just for ion trap architectures. So there's a roadmap question in front of us about how much end-to-end design capability do we invest in and offer, even just for ion traps? Never mind, you know, do we invest in architectural simulation design tools for, you know, quantum EDA type tools for other architectures. Sebastian Hassinger (27:57.045) Hmm. Sebastian Hassinger (28:09.994) Yeah. Daniel Faircloth (28:12.236) and then just extend that across the entire electromagnetic spectrum and it becomes a feast of opportunities. Sebastian Hassinger (28:19.613) Yeah. And a puzzle to be solved in terms of where to put your focus next as a small company. That is always a challenge. I guess, so last question then along those lines, if you'd sort of think out 10 years, are you sort of more imagining null spaces being like the ansys of this space, like an end-to-end, a one-stop shop of all of the tools that you need for Daniel Faircloth (28:29.185) Indeed. Sebastian Hassinger (28:49.033) building the tool chain for your particular technology, your in-house RD, or are you more of like a deep specialist in one particular area that's maybe part of either a federate or even an acquisition by somebody that's putting together that whole suite? Daniel Faircloth (29:07.574) I think one thing that we've seen in the industry over the past several decades, but in particular the past five years is the acquisition approach of the large EDA simulation providers. They provide good solutions, but oftentimes these solutions are sort of bolted together in awkward ways. Users complain about how the how the end-to-end workflows work or don't work. And there's an opportunity there for us to address those pain points that our users are experiencing across a lot of different disciplines, quantum computing and RF being the two that we're focusing on. Long-term, there's definitely a need in both of those marketplaces for more tightly integrated multi-physics. simulation as we see an evolution of the densification, if you want to call it that, of these devices, how they interact with each other, how your iPhone gets hot and you're wondering what is that doing to my iPhone. As an antenna designer, I know it's having an effect. being able to understand these questions and, of course, heating and the Ion trap world is the great nemesis. So how are we modeling the electrostatic fields, the RF fields, the dual heating that's going on in the trap and being able to provide all of that in an appropriate fidelity, one-stop shop for the designers. don't want them to have to go to a bunch of different tools and try to kind of piece together dealing with file format exchange issues. Sebastian Hassinger (30:37.695) Bye. Sebastian Hassinger (31:03.615) break. Daniel Faircloth (31:04.558) In this physics modality, I model these geometric features. In this one, I have to clean up the CAD model in some way. And then you always wonder, did I really model the same thing? We're trying to put all those issues to bed over the long haul. Sebastian Hassinger (31:14.601) Hmm. Sebastian Hassinger (31:20.393) That's crazy. mean, I think about sort of like creative pipelines, in Adobe products, for example, which can be painful. And that's just pushing bits around on a screen. it's just, does it look, does the font look right or not? can't imagine how complex it is when you're talking about modeling an actual system from nature or physics or whatever. That's really amazing. It's quite a challenge. think it's really interesting what you're doing. And as I said, the quantum EDA space I think is is overlooked at the moment. So I'm trying to do these kinds of interviews to bring some more attention to it, because I think it's incredibly vital. So thank you so much, Daniel. This has really been very interesting. I really appreciate it. Daniel Faircloth (32:04.312) Yeah, thanks for having me and thanks for your efforts to educate the world about these challenges. Sebastian Hassinger (32:12.039) Awesome. All right.