Archer Materials Limited (AXE) Earnings Call Transcript & Summary

Good morning, everyone, and welcome to the first half FY '26 results from Archer Materials. Today, we have Simon Ruffell, CEO of Archer, who will provide a short presentation followed by some Q&A. [Operator Instructions] Over to you, Simon.

All right. Thank you, Luke. I'm just going to share my screen. All right. Well, first of all, thanks for joining this webinar. It will be pretty brief, but that will allow for any type of questions, hopefully. All right. Let me get started. So as usual, I'll kick off with Archer. Archer at a glance, many of you probably know the company, but I'll run through it anyway. So Archer Materials, we're the only ASX-listed quantum technology company. And our business model is developing IP for use in areas like quantum computing, sensing as well as medical diagnostics. So we're headquartered here in Australia. As you probably know, most of our core staff are right here in Australia and working in our facilities in Sydney. Our business model is for that core team to be doing the technology development and coming up with the new ideas and producing the IP for the company. And what we do is leverage local universities, local research institutions. And then beyond that, we've formed really productive development partnerships with world-class R&D institutions and technology developments overseas in areas like Europe and the U.S. And what that allows us to do is focus on the technology development without having to run really expensive facilities that are usually associated with developing these types of technologies. So we don't have to run a clean room facility. We don't have to run expensive quantum measurement labs or device fabrication facilities. So in that effort, currently, we have 40 patents filed globally. One thing that has happened and is particularly important here for the first half of FY '26 and then moving forward through into this calendar year, we're significantly ramping up that patent technology development and filing patents regularly now. So we expect to double this over the coming year. And that's a reflection on where we are with the technology development moving into the commercialization phase of these products. And that's because we've finished the early research. And with most of the technologies, the 4 pillars here, we're moving into a true development phase where we're derisking a lot of the technical challenges, solving a lot of the engineering problems to turn these things into real things that can be turned into products, could be licensed to other players. And because of that, that results in lots of patents being filed. So that's an uptick that's going to happen as we move forward. The 4 main projects or technology pillars that Archer focuses on are there in the bottom of the screen there for you to look through. I'll talk through each of these in the following slides. But what I wanted to say is for the first half of this year, what's been fantastic is we've made really significant steps in terms of technology derisking and getting some of these technologies in a position where we can truly start commercial discussions. And that's a focus. It has been a focus of initiating those over the last few months, but also moving forward through to the second half of this financial year and then later into 2026 and '27 to build those. And that's a reflection of where the technology is. So a year ago, we were so early that those sorts of conversations are very difficult to have. And usually, you have to go back to these partners because things are too early. But we've made significant technology derisking and moving into a true development phase for the biochip. The quantum hardware, which is the computing, the qubits and the quantum sensing, again, we've made significant technical jumps forward that will allow us to later this year for -- produce some technology demos that will allow us to then build on that, but start talking to potential partners on this. So next slide, what I wanted to do is just spend one slide focusing on one of our newer projects, which we announced back in January this year. And that's based around quantum machine learning. So the way to think of this is quantum machine learning is a type of software essentially where the specific advantages to running these sorts of algorithms and software on quantum computers. So as part of our broadening of the quantum technology IP portfolio that we have, where we want to move on from only having our exit in one quantum computing basket, but we want to look at software, we want to look at sensing. This is one of those thrusts. And as well as broadening the IP, what we're doing is building up a commercialization road map with our quantum technology that these technologies will fit on the road map at different stages and can be commercialized at different points. And if you just look on the right-hand side there, there's quantum machine learning for fraud detection, the strategy here is to start building up more quantum technology around the computing, but look for opportunities to commercialize earlier than some of the hardware aspects of the quantum technology. This project originated the middle of last year, so beginning of the financial year. We began discussions with CSIRO. They happened to host one of the world leaders in developing quantum machine learning algorithms. So we discussed various applications with them and then kicked off a project in January. This initial project to look at this technology and assess where we will go with it will last 1 year. We're expecting around the middle of this year, so June, July of this year, we should start seeing initial demonstrations of the machine learning algorithm, but also feeding in real financial data that we'll start looking at credit card signature fraud as well as fraudulent transactions within accounts. And once we start running these algorithms on that real data, we'll be to benchmark the quantum machine learning algorithm and the fraud detection against current incumbent traditional technologies. And the idea is there at the end of the year, we have full demonstration of the software and the advantages of the technology that we can then start talking to potential customers. That could be software houses that supply, say, big banks or it could be the banking systems themselves. And as we start validating that sort of software and utility and advantage, we're not stuck with this initial application of fraud detection. This is a stake in the ground. It's the first application to go after. But what we'll go to do is develop the framework from this initial application and start moving into very -- into other high-value applications and some listed here on the slide, there's applications in health care, defense as well as more cybersecurity areas. So new initiatives, it's really exciting. It's another part of building up our commercialization road map around quantum. And there will be news flow coming through in the next few months on the status and progress with this project. So I'll just move on now on to an overview of the first half of the financial year. And I'm going to focus on the technologies, the technology development and also where we're positioned now to start talking with potential commercialization partners. And I'll start with the quantum hardware. So essentially, overall, there's 2 things that have happened in this first half of the year. As I've already said, we've really advanced with some technical derisking around the technologies. Developing a quantum qubit and at the same time, demonstrating that it's superior over other qubit platforms is extremely challenging. The time lines we set ourselves are extremely challenging, but we're making fantastic project progress there. And the highlights have been around readout of our carbon-based qubits. That work's continuing. I saw some fantastic results just this morning from work from the team here. And as we keep moving forward there, we'll be moving towards that initial qubit demonstration, which will be a real significant milestone for Archer. It will allow 2 things. One, we can start talking to potential partners about using the technology on their broader, larger full-scale systems. And also we'll be to continue then we'll move into a phase where we're optimizing and improving the performance and continuing to prove out the manufacturability of the qubits, which is the key building block and component of a quantum computer. The other activity we've really focused on this first half year, as I've already said, is portfolio broadening of the IP as well. So we've ramped up projects in sensing in addition to the TMR and also the quantum machine learning activities that I've just talked to. So with the TMR sensing, one thing we're deliberately being very careful about is finding the right application in the market and building the strategy around that. And one key bit of data from earlier in this financial year was that we demonstrated good TMR operation in cryogenic environments. with the sensors that we source from our foundry in China. And that opens up applications in areas like quantum computing, but advanced sensing and aerospace and space exploration. So there's a lot -- there's a large area there to explore. And our main activity over the last 6 months has been talking to potential end users and customers and assessing where the markets are, where we can have the biggest impact with these technologies. And at the same time, that's also allowing us to look for other sensing applications that leverage our carbon material platform that we're developing for the qubit because as we develop the qubit, a lot of that information and device structures and learning that we get there can be transferred into a carbon-based sensing that has a lot of advantages over other technologies. I won't repeat what I said about quantum machine learning, but that should continue to grow as we continue to get positive results throughout the year. So again, this is moving us towards having demonstrating demonstratable hardware that it demonstrates the technical entitlements, demonstrates the manufacturability and starts allowing larger -- other customers that are building larger systems to take this technology, take these components and put them into the system and enable their technology, which is extremely high value. With the biochip, I would say the main activity there and main change in the last half year is that, again, I'm going to use technical derisking, but part of that technical derisking has been to get out of the research phase of the biochip development and move into a phase where we're making demonstrators and prototypes that are then being developed It's engineering work now, not research to move towards something like the picture on the right-hand side of the screen. What that's also allowing us to do is build data sets from real things that could be used by people currently, even though it's still fairly early lab prototypes. But because we're gathering that data and we've done a lot of the technical derisking, it allows us now to move into commercial discussions with potential partners, something we haven't been able to do before. So something we're ramping up now and we'll be a report on later in the year is that we want to start talking to pharmaceutical companies for early trials and use in developing their drugs. We also want to start talking to medtech companies about joint commercialization from where we are now through clinical trials through to getting it out to the market. And we've only been able to do that over the last couple of months because of the technology derisking and the prototype status of the system. The other key activity, I would say, in this area in FY '26 was demonstrating feasibility that the biochip can also be used at least for our early application here for our potassium sensing. We can use silicon-based biochips. And what that does, it completely derisks later on the supply chain issues associated with graphene. So graphene has -- it has really attractive properties. We're looking at using it continually through after the potassium for later-stage applications. The one huge risk at the moment is it's still fairly early in terms of technology development and the supply chain there for high volume is not in place. So we significantly derisked the potassium sensor by demonstrating the silicon feasibility with IMEC. And as we continue through this year, now we'll continue working with IMEC. We're reaching out to contract manufacturers and contract prototypers to turn the alpha lab demonstrator that we showed in January this year through to a beta demonstrator that can be used for preclinical testing and still with the goal at the end of the year is to have something that could be used for clinical trials. And we've been heavily engaged with regulatory experts in the U.S. to build our strategy and start collecting the data sets and the packages that we need for a pre-submission meeting with the FDA. But all of this is on the back of the technology development. That's what comes first. We have to -- we've reached the stage where we've derisked. We have early alpha prototypes, and that allows these discussions and planning with the regulatory process. The other fantastic thing in the middle of last year, we started some early feasibility data for testing other ions in liquids. One that we're pursuing at the moment that we're building the data set and starting to assess the market is testing for lithium in blood. Similar to the potassium, there's a real need for a point of care or even an at-home test there for patients that have mental health disorders like bipolar disease. So again, like the potassium, there's a potential market there. It's exciting. What we can do as we continue down the path of the potassium as our beachhead application, all of that learning we can leverage to rapidly make a lithium biochip product as well. And we'll continue building these technologies, these applications beyond lithium as well. So there's a pipeline of applications that we're building on this biochip platform. So I'm coming to my last slide now, which is the outlook here. This slide, many of you have probably seen before, but it's the 2026, so FY '26 and then moving into FY '27 key milestones. Just laid out here, I won't talk about it too much, but along the top there is our biochip product. So as I've already said, we had an early alpha prototype demo, which has unlocked being able to get out there and initiate some commercial discussions. We'll be working towards beta prototypes in the middle of the year that will be ready for preclinical trials and then a final prototype towards the end of the year with the aim of being able to get into clinical trials in 2027, which will be a major hurdle to get over. The other thing that I've talked about there, I talked about lithium, but we're also working on other feasibility data for other ions for other applications. And we expect that in the coming quarters, we'll be -- we'll have early demonstrations of what I'm calling the next-gen biochip, but it will be the biochip with a different application space, probably lithium at this point. And that's just the next -- that will be building the pipeline of products that follow behind the potassium. As far as quantum technologies go, we're still working towards that qubit demo towards the middle of the year and then continuing to build on that test from quarter 3 to quarter 4. And again, a bit like the biochip, once we have that data, that will unlock discussions with potential partners to start getting feedback on the technology and start investigating how we get to a licensing agreement with somebody else. The quantum machine learning demo, as I said, it's a year-long project. I've got a quantum machine learning demo marked here at the end of 2026, but we should have early indicator data in the middle of the year. We're identifying sensing applications. And just like the qubit demo, we expect about 6 months behind that, we should have a sensing prototype of some form that again, will unlock those potential commercialization licensing discussions with partners. So really important here. As you can see here, we're transitioning away from very basic research into real prototype product development processes, at least for the biochip and the quantum is probably a bit behind that. But again, it's transitioning away from very early lab research through to what we need to do to turn these things into licensable technology. I'll just finish, there's a disclaimer here for these slides. I'm not going to comment on that. I'll just scan over it, and thank you for listening. It will be great to receive some questions.

Thanks, Simon. It's Luke here. So I'll just start with some questions. What is the latest third-party funding and/or government grants funding?

The latest? Well, I mean, what I can comment on is that where we've been -- I mean, I think I said this last year, but we've been -- since the middle of last year, since the beginning of this financial year, we've been actively applying for grants funding. It's a no-brainer. There's various schemes out there. We currently have a couple of applications under review. So we're pretty hopeful for one of them. But again, that will be something that as soon as we receive government funding, that will be a great boost of programs. That will be news that we'll put out there. But again, we have a strategy for the rest of this year and moving into next year for government funding that we'll go after and the projects associated with those. And because we continue to build these really good partnerships as well with people like Emergence, it opens up opportunities for joint grant funding with those companies as well to bolster those partnerships.

Thanks, Simon. Beyond fraud detection, you mentioned some of them, but what are some of the other possible applications you are mostly likely going to go for with QML?

Well, we're assessing those. So just I can provide examples of this space because there's some -- I mean, the area has some hugely high-value applications. So some of those are in the health care space. There's a lot of AI companies that are sprouting up already related to health care where they might monitor things like heart scans or cardiovascular imaging where the AI then takes those and runs analysis on them. And it can sort of catch things that maybe the doctor or the surgeon doesn't catch or to effectively improve the outcomes of patients. And that's a big thrust with AI. Now that reaches a point where it's limited in its sophistication and it gets more and more difficult to sort of be very accurate and detailed there. If you add quantum machine learning algorithms to some of that image recognition and image analysis, there's some really interesting ways that you can look at that information in these huge multivariable spaces that a normal computer can't with AI. And again, there's lots of applications there, all related to improving health outcomes or diagnosis for patients. So you can imagine there's a large space there. And because we're not developing hardware, it's faster to products so well with health care, even with software and quantum machine learning, there's rigorous regulatory burdens there. Another area is things like logistics, like it's -- many of you might have heard of things like the traveling salesman problems that are incredibly difficult to solve by a conventional computer because they get very big, very quickly. In terms of, say, logistics of public transport, these quantum machine learning algorithms are really well suited to solve those problems efficiently and at very large scale. So we could be looking at maybe New South Wales Sydney Transport, for example. They've got lots of problems, timetable and trains, buses, all that sort of stuff. And you can imagine once you start looking at all the combinations of all those individual bus routes, train routes, light rail routes, for example, those problems are really difficult to solve. But quantum machine learning is well suited to that. And CSIRO already have some activity in that area. So again, that's a couple of examples. They all have different values. There's pros and cons there, and that's something we're mapping out as this fraud detection work progresses.

Great. Thanks, Simon. Could you let us know how some of your commercial partnerships are progressing?

Yes. I mean as I've said, we've got to the technology point where we've unlocked some of those commercial discussions or being able to begin those, especially for the biochip. So we've started those. This is something we've been ramping up over the last month or so since we got the alpha prototype for our biochip, for example. That's going to continue in the coming months. And as we make progress there and any agreements that we get into, that will be news flow coming out. But that's progressing pretty well. And then beyond that, once we get to July, August and we get things like the qubit demo and we start getting early data from the quantum machine learning, the same thing can happen with those as well. We can start those in early discussions with potential partners. So -- but short answer is it's early days, but we're at a position now where we can start that, and that will definitely be a focus for the remainder of this year.

Great. Thanks, Simon. Could you let us know how the TMR sensor with quantum could boost defense and aerospace capabilities?

Well, that's a good question, and that's something we're looking at it. When I said we're talking about potential end users and partners and mapping the application space. That's what we're looking at. I can give one example of something that we're discussing internally and talking to potential partners about is for defense, a big area of interest is GPS staff navigation. So if you look at everything that's going on in the world, now there's drones flying all over the place. They rely on GPS satellite signals for navigation. Often, those signals are jammed or in a war zone, those things are -- the drones are starved of those satellite signals. So there's technology being developed that allows navigation to happen without those GPS satellite signals. And you can do that with something like TMR because it's an extremely sensitive magnetic sensor, it can detect U.S. magnetic fields very easily. fairly accurately and map those on to maps of magnetic fields around [ vehicles ], and that allows you to navigate that way. So that's one example. But there's various other things out there that we're actively pursuing as well. And moving that -- in terms of commercial partnerships, we are actively at least it's on our radar to be trying to work with defense in some form or the other. It's pretty difficult to get into. And many times, they're looking for more established technology or more mature technology than we have, but that's a work-in-progress, and we will continue to try to get into that space.