About Aravind Ratnam, Chief Strategy Officer at Q-CTRL
Aravind is a deep tech leader with over 20 years of experience across aerospace & defense, semiconductors, software, consulting, autonomous driving and quantum technology. He is currently the Chief Strategy Officer at Q-CTRL. In his role, Aravind oversees strategy, revenue, product, and partnerships, working across Q-CTRL and overseeing some of the industry’s fastest growth.
Aravind’s Links
www.linkedin.com/in/aravindratnam
Q-CTRL’s website: https://q-ctrl.com/
Ironstone Opal, Q-CTRL’s field-validated quantum sensing system for navigation: https://q-ctrl.com/ironstone-opal
Black Opal, Q-CTRL’s quantum learning platform: https://q-ctrl.com/black-opal
Fire Opal, Q-CTRL’s quantum computer performance optimization software: https://q-ctrl.com/fire-opal
Boulder Opal, Q-CTRL’s quantum control software: https://q-ctrl.com/boulder-opal
Summary:
Episode 88 of DIY Cyber Guy tackles a fast-emerging aviation risk: deliberate GPS disruption. Host David W. Schropfer opens with the August 31, 2025 incident in which European Commission President Ursula von der Leyen’s flight experienced GPS interference near Bulgaria. He explains how GPS works, why it fails under jamming or spoofing, and how crews can revert to paper charts and non-GPS radio navigation—useful, but not a long-term answer in a world where signal denial may become routine.
David’s guest is Aravind Ratnam, Chief Strategy Officer at Q-CTRL. Aravind distinguishes quantum sensing from quantum computing and explains how quantum sensors read minute variations in Earth’s crustal magnetic and gravitational fields, then match those readings to high-resolution “maps” to determine position. The key advantage is stability: classical inertial sensors can drift to an uncertainty of roughly one square mile after about eight hours, while quantum sensors can maintain that same accuracy for roughly 1,500 hours—an endurance shift that transforms navigation at sea, in the air, and underwater. He outlines which environments favor magnetic versus gravitational mapping and notes that quantum sensing is no longer a science experiment; it is an engineering and integration challenge.
Aravind also surveys the state of quantum computing—error rates, suppression software, emerging use cases in optimization—and how future data centers may seamlessly allocate workloads between classical and quantum resources. Q-CTRL’s portfolio includes Ironstone-Opal (quantum-assured navigation), Black Opal (education), Boulder Opal (design tools), and Fire Opal (error suppression), supported by partnerships with organizations such as Lockheed Martin and Airbus, and a DARPA award. Full links and resources are available, below.
SHOW NOTES:
#88 – Why We Need Quantum Sensing to Support GPS
Hair on Fire 3 out of 5
Everyone who ever plans on getting on an airplane someday
On August 31, 2025, European Commission President Ursula von der Leyen boarded a plane to Bulgaria. As her plane approached Plovdiv International Airport, something went wrong: GPS was malfunctioning. An investigation would later prove GPS was not malfunctioning – it was being jammed, allegedly by Russia (which denys the allegation).
SOURCE: https://www.gpsworld.com/plane-carrying-eu-president-hit-by-alleged-russian-gps-jamming/
GPS functions by using signals from at least four satellites orbiting Earth. Each satellite broadcasts its position and precise time. A GPS receiver calculates its distance from these satellites by measuring signal travel times, then triangulates its exact position—latitude, longitude, and altitude—based on the intersection of those distances. In other words, if your GPS receiver has a direct line of sight to at least 4 satellites, it should function properly.
This system relies on a signal being sent by a ‘device’, and the same signal being received by another device.
But what if that signal is Jammed?
Jamming a signal can happen different ways, such as: (1) Broadband noise jamming, which overwhelms satellite signals with stronger radio noise on GPS frequencies; (2) Spoofing, where false GPS signals mimic real ones to mislead receivers.
Luckily for President von der Leyen, navigation was created before the invention GPS, and after the invention of paper, so the pilots used paper maps and non-gps signals to navigate to the Bulgarian airport and land safely.
NOW WHAT?
If jamming GPS represents a new form of warfare, then we need to start thinking about a new form of GPS.
What if we could use quantum sensors to detect minute variations in Earth’s magnetic and gravitational fields, matching them to known maps? That’s exactly what is happening with a quantum-assured navigation system called Ironstone Opal, which was created by a company call Q-CTRL.
Here with me to discuss this today is Aravind Ratnam, Chief Strategy Officer at Q-CTRL.
Aravind is a ‘deep tech’ executive with over 20 years experience in aerospace, defense, semiconductors, software, and quantum technology.
Welcome Aravind!
Starting at 30,000 feet – why is a quantum sensor better than a traditional computer sensor for navigating without GPS?
TRANSCRIPT
0:02 – David W. Schropfer
Welcome back everybody to DIY Cyber Guide. This is episode 88: Why We Need Quantum Sensing to Support GPS. Now, this is a hair on fire three out of five, really for everybody who ever plans on getting on an airplane ever again. And I’ll explain exactly what I mean by that. So here’s the story.
On August 31st, 2025, European Commission President Ursula von der Leyen boarded a plane to Bulgaria. As her plane approached the international airport in Bulgaria, something went wrong. The GPS was malfunctioning. Now, an investigation would later show that GPS wasn’t malfunctioning. Instead, it was being jammed, allegedly by Russia, but Russia denies that allegation. Regardless, here’s how GPS works, for those of you who don’t know. Basically, GPS functions by using signals from at least four different satellites orbiting the Earth. Each of these satellites broadcast its position and a precise time, And remember, these signals are moving at the speed of light. So that time is precise to, I think, 1 ten millionth of a second. And each of these GPS receivers calculates the distance from the satellites by measuring the signal travel times, again, at the speed of light. Then it triangulates the exact position, latitude, longitude, and altitude. So with all of those different data points, you can pinpoint exactly where that receiver is either on the surface of the Earth or in the skies above the Earth. So in other words, a GPS receiver has to have a direct line of sight to at least four satellites to receive the signal from at least four satellites in order for it to function properly. And of course, this system relies on that signal being sent by one device, the satellite, and being received by another device, your GPS receiver. But what if that signal is jammed, what happens? Well, jamming that signal can happen in a bunch of different ways. A couple of those are something called broadcast noise jamming, which just simply overwhelms the receiver with multiple signals on that same radio frequency, essentially, blasting lot of different messages at the same receiver. So it can’t parse out, or it can’t find, or it can’t receive the proper signal from the satellite as it should. Another is spoofing. Where a false signal that’s intended to mimic a GPS signal, giving false information, and therefore giving you a false location as to where you are, is another way to fool the holder of a GPS receiver into thinking they’re somewhere that they’re not. Either one of those things is bad. Now, luckily for President von der Leyen, the navigation was created before the invention of GPS and after the invention of paper. So the pilots were able to use paper maps, non-GPS signals, and old-fashioned aeronautical navigation techniques to safely land that plane in the Bulgarian airport as planned. But that leaves a larger question of, now what? If jamming GPS represents a new form of warfare, then maybe we need to start thinking out a new form of GPS. So the question’s out there, what if we could use quantum sensors to detect minute variations in the Earth’s natural magnetic and gravitational fields and match them to known maps? Well, that’s exactly what’s happening with a quantum-assured navigation system called Ironstone-OPL, which was created by a company called Q-Control. So here with me today all of this today is Aravind Ratnam Ratnam, Chief Strategy Officer Aravind Ratnam is the, I’m sorry, let me say that again. Aravind Ratnam is a deep tech executive with over 20 experience in aerospace, defense, semiconductors, software, and of course, quantum technology. Welcome Aravind Ratnam.
7:43 – Aravind Ratnam
Thank you, David. Great to be here.
7:46 – David W. Schropfer
Great to have you on the show. I’ve been looking forward to this conversation. So starting at just a 30,000-foot view, why is a quantum sensor potentially better than a traditional computer sensor at navigating without GPS?
8:01 – Aravind Ratnam
So look, traditional sensors are prone to drift. Take a string and stretch it. You stretch it enough, eventually it will lose some of its elasticity.
8:14 – Aravind Ratnam
In the physical world, if you build inertial systems, magnetometers, gravimeters, like the ones that your phone has, with these classical sensors, they drift over time. And the drift just keeps on building up. So over time, where think is not exactly where you are, right? And it’s just physics. Quantum sensors rely on atomic principles. And if you remember some of your high school physics or college physics, I forget what it is, new type of rules, where atomic transitions have strict rules as to the level of energies. The amount of the manifestation in a quantum sensor is that you can have an exquisitely sensitive, but also ultra-stable sensor that does not drift. And that is fantastic. So a ship that uses today’s navigation systems today, just relying on navigation systems After eight hours of drifting, it’s going to be maybe within mile of uncertainty. Whereas if you build…
9:20 – David W. Schropfer
Eight hours can lead to an entire mile?
9:22 – Aravind Ratnam
Eight hours of drifting, it’s going to be within one square mile of open ocean, which is quite a big thing. Like if you’re trying to find a mechanic submersible within one square mile of ocean, that’s a lot. I see. Sure, yeah. But you do the same thing with a quantum sensor. You can take that to 1,500 hours and be within that same one square mile. So essentially, you can outlast a war using these ultra-precise, ultra-stable, largely calibration-free quantum sensors.
9:50 – David W. Schropfer
So you’re saying, and just to make sure I’m following the examples, if a ship had no navigation with the outside world, it just knew the place that it was at the moment that it started drifting, within one hour, it could be anywhere within a square mile with traditional sensors. But with a quantum sensor, the number is 1,500 hours before it gets to an area as large as one square mile. Is that about right?
10:18 – Aravind Ratnam
Yeah, with one modification, any ship worth its salt will have a backup to a GPS. And that’s the system. And that’s what’s comprised of magnetometers and accelerometers, sometimes gyroscopes.
10:32 – Unidentified Speaker
Just relying on them, it’s going to be within that one square mile after eight hours, whereas with a quantum sensor, it can be, again, within that one square mile after 1,500 hours. What’s better?
10:44 – Aravind Ratnam
1,500 is better.
10:45 – David W. Schropfer
1,500 is better, yes. No question.
10:50 – David W. Schropfer
And describe what’s happening. And my audience has a general idea of the fact that quantum computers aren’t a better traditional computer. They’re a different computer. They simply work on a different function based on a qubit and not on a a traditional 1 or 0, a traditional bit. But again, perhaps starting from 30,000 feet, what is the sensor actually doing? What’s its basis for a point in space? And how does it maintain that point in space as the point itself changes?
11:24 – Aravind Ratnam
Yeah, so first of all, let’s not mix quantum computing with quantum sensing. They’re two entirely different things. You can even call quantum sensing an atomic sensor. If you want it.
11:35 – David W. Schropfer
It’s not about putting a quantum computer on a ship, right?
11:38 – Aravind Ratnam
It’s a completely different thing. A quantum computer works based on qubits, which we can explore separately. But to answer the question that you have, there are different sensors that use different types of effects. We use a particular type of sensor that uses certain polarization effects to understand the change changes to the local magnetic field. So if you remember, you go back to your Boy Scouts, where you use a compass to detect North and South.
12:13 – Unidentified Speaker
That signal, North and South, comes from the core of the Earth. Earth is a sphere, and the core is in the very, very center, the heart center. Now, there’s a separate magnetic field that’s caused by mineral deposits that are close to the surface of the Earth.
12:27 – Aravind Ratnam
Those are called as crustal deposits. Now, those mineral deposits that we dig or often, you know, this is what the mining business is for. Those mineral deposits create their own magnetic field. That’s the crustal magnetic field. That is what we’re detecting with quantum sensors, not the core, not the much more stronger signal, or, you know, that’s akin to what your fridge magnet, in the order of what your fridge magnet can produce. This is a much smaller signal, and you need a much more sensitive sensor to pick that up. That’s what we’re talking about.
13:01 – David W. Schropfer
So it’s the fact that their quantum sensor is sensitive enough to pick up the weaker of the two magnetic fields. Can be correlated to a known map, I would assume.
13:14 – Unidentified Speaker
Now, yeah, that is where the trick lies. You’ve seen Google Maps, and if you turn on Earth mode, you see all these, the terrain data and all that.
13:22 – Aravind Ratnam
It turns out that you can create magnetic maps of the crust, and they’ll have these squiggles and lines. And there’s an incredibly rich amount of detail in these magnetic maps. So if you can fly your quantum sensor around and essentially localize yourself to where you are on this magnetic map, essentially you can locate yourself on the planet, right? And theoretically to a few meters of accuracy, which is more than enough for what you need for most navigation purposes.
13:59 – David W. Schropfer
So does that imply that each point on the globe has a unique crustal magnetic signature?
14:10 – Unidentified Speaker
So if you look at the local signature, the way each point will be relative to its peaks and valleys and squiggles and everything is completely unique. So I urge you to go on. There’s various open source maps of the world. And yeah, it’s kind of a new world in itself, right? And it turns out that there’s also a gravitational map of the Earth that is very, very unique.
14:35 – Aravind Ratnam
And so Q-Control does not just magnetic navigation, but also gravitational navigation. And it turns out that gravitational fields are better for certain things like water, over water, or underwater vehicles. And airborne applications are better for magnetic navigation. Magnetic navigation in many cases. So we do a combination of both in the company.
14:59 – David W. Schropfer
So I would imagine since the moon imposes the biggest gravitational pull on the Earth other than the sun itself, I got to believe that this is just a lunar navigation on steroids, if we want to simplify it.
15:17 – Aravind Ratnam
And the consideration that we have, and by the way, there’s all kinds of effects like space weather that we have to compensate for. But I’m not going to bore you with the details.
15:27 – David W. Schropfer
So for my listeners, Q-Control is not a sponsor. This is not a pay-to-play podcast in any way. But I’m fascinated by the work that they’re doing, how it applies to a real problem, which is problem number one that we’ve been talking about is GPS navigation and the vulnerabilities of that technology, especially in a hostile world or a world that has hostile hostility in places. But I’m fascinated with how did Q-Control get from your core quantum products, which I understand to be a software product that I’d love to hear more about, to quantum sensors?
16:06 – Aravind Ratnam
So Q-Control really is known for its excellence in the discipline of quantum control. If you have done electrical engineering, there’s this field called control engineering. And I, in fact, I used to be a control engineer way back. And this is the quantum equivalent of it. Look, any quantum body is, you know, let’s say you’re able to trap an atom.
16:35 – Aravind Ratnam
To trap an atom and keep it that way poses several challenges. And then one of those challenges is that it is very, very sensitive to the fields around it. It’s sensitive to the noise, vibration, harshness around it. It’s sensitive to magnetic fields. The academics in the area like to quote that, hey, if you have an atom trapped on the 30th floor of a building and you run your elevator from the first floor to the second floor, there’s enough magnetic current in the rails to disrupt the atom on the 30th floor or something to that effect. The details matter here. But what I’m trying to say is that whenever you deal with atoms, you deal with noise, OK? Is to work with that noise and actually suppress it, and then not just compensate for it, but to actually characterize it and be able to suppress it so that your systems can work in an error-free manner. In quantum computing, that is transformational. Today, you know, people talk about 100 1,000 qubits, you whatnot. The thing is, the utility of these qubits only matters if you’re able to work with the error. And so error correction has become this hugely important tool In fact, it is the most important problem in quantum computing. And error correction, just to be semantically correct, has several disciplines out of which we specialize in something called error suppression in quantum computing. So imagine you have a computer made by IBM, Google, whoever it is, and it has lots of qubits, operates really fast. But the problem is that you’ve got all this noise. So if we are able to suppress the noise, we are able to make the hardware work at its fullest potential. Now, when you take this discipline and translate it to quantum computing, quantum sensing, you can take advantage of that noise. It actually translates as sensitivity, right? So the vibration-induced issues that I was talking about manifest in a different way in quantum sensing, right? So you can actually make these sensors that are very, very sensitive to changes in magnetic and gravitational fields, right? And so that was the genesis of the idea. We are one of the probably two companies in the world that do quantum computing and quantum sensing. So we started with this idea of quantum control, and that’s why the name of the company is DoControl as well. That started it all.
18:59 – David W. Schropfer
Seems to definitely fit. When we’ve talked about quantum computing on the show in the past, I’ve essentially described the error correction as a quantum computer will run the same, try to the same problem multiple times and the answer that appears most often effectively wins. Or at least that was the nascent days of quantum computing, which is terrible. You have to basically, you’re basically guessing that the answer that pops up the most often must be the right one because it came up more than once or more than twice. Than any other answer because of the issues that have to do with trying to keep an electron close to absolute zero for a period of time and using the different types of technologies that are out there. Very unstable, a lot of noise. Now, bring me up to date and bring my listeners up to date on the state of the art of error correction because I know we no longer need to approach absolute zero for the temperature of a qubit for it to function properly. There are lots of different technologies, different types of hardware. And you’re a software company, if I’m not mistaken, right? So how are an industry on the error correction problem? And how does that translate across multiple different types of devices with different types of needs?
20:27 – Aravind Ratnam
So we have been fortunate in that we are a picks and shovels company. So as you know, whenever a new industry is created, the picks and shovels tend to be the ones who move quite fast, at least in the earlier stages of the industry. That’s the first thing. The second thing is, how do you think about error? So if you think of a semiconductor chip, the chances, the probability of error is something like one in 10 to the 23 operations. So if you run 10 to the 23 operations, chances are you’ll have one flip bit. Due to an inherent fault of the semiconductor. I’m not talking about cosmic rays. I’m not talking about anything else. They’re totally different effects.
21:13 – David W. Schropfer
So that’s one context.
21:14 – Aravind Ratnam
The second context is, if you remember when the iPhone came out, there were a lot of dropped calls, right? So this had to do with packets being dropped in your conversation when you’re talking to yourself and all that.
21:27 – Unidentified Speaker
And so the telecom industry went to this concept of seven nines of reliability.
21:33 – Aravind Ratnam
And one of my previous companies, Wind River, which was part of Intel, provided an appliance, an infrastructure software appliance that actually enabled that seven nines of reliability. And so what we are seeing in the quantum world is not very dissimilar. Today’s error rates are somewhere between one in 1,000 to one in 10,000. It depends on who you ask and which system it is and all of that. But what’s very interesting is that with software with the kind of software that we produce, you can actually start to improve that. And by the time you are in the realm of one in a million, you start getting real serious. And what I mean by serious is now you can start running real algorithms and start getting real world results. I’m not saying you’re going to soon have quantum computers in production, that that time is coming.
22:22 – Unidentified Speaker
When I talk about production as in a data center where you have real business running on it, for example, running on it, et cetera, et cetera. That day is coming, but I’m not going to take a bet here on exactly what that day is going to be.
22:36 – Aravind Ratnam
But the thing is, once you are in that region between one in a million to one in a trillion, which is the 10 12, that is where the money is, right? Especially as you approach that one in a billion, one in a trillion number, you are now starting to increase your trust in the system and you’re able to run algorithms And yeah, so maybe, you know, it’s going to be a long time before we get to that one in 10 the so, 23 but the industry is fast pushing towards that one in a million to a one in a billion point. And by the way, don’t just go off of error alone. There are certain systems like the ones, like the trapped ion systems, like what IonCure, Quantinium makes, where you can get to really, really good error rates. But the trade-off is they may not be as fast as some of the other systems, right? You not just need a good system, but you also need something where the throughput So yeah, quantum computing is a fairly evolved field. It’s a fairly sophisticated field.
23:44 – Unidentified Speaker
There’s a bunch of metrics that we need to track, not just gate fidelity, but also gate speed and coherence time and all that, yeah.
23:51 – David W. Schropfer
I really admire the way that you just the threshold from a practical standpoint, which is when quantum computers are running in data centers, just like other types of computing devices, which we all know as servers today, and they’re being used as a matter of course, by companies that are willing to rent them in an AWS rack effectively, although it wouldn’t necessarily be in a rack per se, but from AWS or an Azure environment where you’re trying to just rent into the computer power that you need to do the problem solving that you want to do, that’s the threshold. That’s the threshold where the computer is reliable enough and the answers it produces are trustworthy enough that you can use it in business and not second guess every answer that comes out of it, which is true for traditional computers and different traditional server farms. We trust what comes out of it because the error rate at 10 23rd power is not going to affect almost business vision that I’ll see in my lifetime or anybody listening to this will see in their lifetime. So I understand that quantum computing has to get there. Where would you say we are on the continuum based on what you’ve seen? Have you seen companies, for example, do some business trials where they’re using quantum versus traditional perhaps to draw some conclusions to see if they can start to bring in quantum into their into their day-to-day cycles, or is this all still in a version of test phase?
25:25 – Aravind Ratnam
So the first thing that people need to realize is quantum computing is suited for a specialized class of problems.
25:39 – Aravind Ratnam
Don’t ask a quantum computer to do a big data problem or one of the problems that your normal CPU or GPU might be able to execute really, really well. There are other class of problems, the so-called combinatorial optimization type problem, like your traveling salesman type problems where you don’t have that much data, but there’s just so many variables. As an example, 100 trucks need to deliver a million packages to 100,000 houses. What is the most fuel-efficient route to do that, right? And so specialized in those particular types of problems. So where the industry is coming to your problem today is it’s an active investigation mode on the types of real world use cases. So the industry has evolved from certain toy problems that were specialized for quantum computers that you are almost like, you know the design of a quantum computer or what it’s meant for. Using the theorems of quantum computing, and then you’re designing problems around it. Okay, so that’s a start to the industry, but that’s not real. Now it’s getting to a point where, for example, Google released this thing called Quantum Echoes, and it’s a particular type of algorithm that may have, that may very well have real-world implications. Now, what we’re trying to do is taking these real-world problems and then trying to see if we can get a quantum advantage. Quantum advantage means it’s just more efficient to use a quantum computer a classical computer. And that’s really where the industry is. There are two or three industries, financial services being one, chemistry being another, where there are certain types of problems that are really applicable to quantum computers. And that’s where a lot of the attention is today. And so would dare say that there’s, I’m sure there’s a distribution, but there’s a whole bunch of people looking at these sectors a little bit more closely than they’re looking at these other sectors. But despite the narrowness of the problem, the level of potential impact is just so high that it is absolutely, absolutely worth it. Because if you lose out the damage to not just the economy, but the sovereignty of countries, it’s just so high that that people are really scared of, or countries are really scared of getting left behind. So everyone’s investing right now.
28:14 – David W. Schropfer
And that right there is the so what question when it comes to, it’s one of the first questions you have to ask when looking at any technology. So what? What can it possibly do? So how high is high? How would you describe the delta between the ultimate peak capabilities of traditional computing and the types of problems it can versus the ultimate upon arriving at full maturity that quantum computers can offer?
28:44 – Aravind Ratnam
What’s the gap between those two? If you took the world’s best quantum computers, let’s say the ones made by IBM, and I’m not necessarily saying they’re the best, but they’re one of the best.
28:55 – David W. Schropfer
Good example.
28:56 – Aravind Ratnam
You took the world’s best error suppression software, like the ones that we make, and you run them on certain classes of optimization problems we have shown shown that we can come very, very close to the best classical solvers in the world. And this is a presentation I made recently as well with a in Japan. And so the answer is we are getting really, really close on those specific types of problems. What’s going to happen is that we will achieve parity, or maybe we’ll get to 110% of what, for example, a tensor processing unit from Google can achieve with the best optimization solver in the world. And what we’re going to do is we’ll try to expand that advantage to other use cases. And over time, that number will grow from 110 150, 200 in terms of processing speed and accuracy, and across multiple use cases. Where, okay, it then makes sense to start nominating a certain amount of space and data centers to quantum computers. When that happens, it’s going to put pressure on the industry to lower costs, like, you know, assembly lines will start getting set up for quantum computers. Costs will come down, quality will go down, and supply chains will get established. And, I mean, quantum computers are already on the cloud. They’ve always been on the cloud, and they’ll continue to be. To be on the cloud. So I would say that the average person, the average user, will not even know that he’s using a quantum computer. There will be software in the middle. You take a certain problem, there will be software in the middle that will partition problems into, here is what’s optimal for a classical computer, GPU, CPU, and here is the quantum piece of it. It’ll all get done in a manner that is completely transparent to the end user. And this is how proliferation will will happen.
30:57 – David W. Schropfer
So despite the power usage and the efficiency disparity today, you think ultimately quantum a cost savings overall in just computer processing categorically?
31:13 – Aravind Ratnam
Computer processing and the corollary, the energy usage will come down.
31:21 – Aravind Ratnam
But I’m not saying that will happen in a vacuum.
31:24 – Unidentified Speaker
things will get more efficient over time.
31:26 – Aravind Ratnam
And because of that, you’ll just use less energy in burning it. Because I know that there’s a very specific angle that looks at it from an AI perspective. Hey, AI is burning so much power, can quantum. I honestly, so there’s two ways to look at the intersection, which is AI helping quantum and quantum helping AI. Today, we are using AI to help our quantum work. In the future, there going to be an intersection that intersection may happen a little bit later because certain technologies will need to get developed first, including more efficient language models and such. So, yeah.
32:06 – David W. Schropfer
So you think AI… Did I hear you say that you think that AI will eventually run more efficiently using a quantum or, I guess, a traditional computer that’s augmented with quantum computers, or do you think AI can just run on quantum computers at some point in time?
32:31 – Aravind Ratnam
It’s of active research, and the quantum community, as well as the AI community, sure hopes that this will be the case. And I know that a lot of smart people are working on the problem, so we don’t know yet. And my personal take is it will happen one day. It’s just a matter of time.
32:47 – David W. Schropfer
Thinking about the assistive technology that can evolve as quickly as AI with a system that has, again, fully matured with error correction, really getting under control, that has the kind of power and speed of a quantum computer is really mind-boggling what the potential is.
33:08 – Unidentified Speaker
And this will probably be beyond the singularity. And I think we need to think about societal implications.
33:16 – Aravind Ratnam
The other side of the black hole? No, no, no. Singularity is in the context of artificial intelligence, where machines become smarter than men.
33:27 – Unidentified Speaker
Ah, general intelligence, yes.
33:28 – Aravind Ratnam
General intelligence, uh-huh, yeah.
33:29 – David W. Schropfer
Yeah, topic onto itself, which is more worrisome, I think, than it is.
33:38 – Unidentified Speaker
But I will mention that, because we covered both quantum sensing and quantum computing, I can confidently say that quantum sensing is here. With quantum computing, there may be a tiny bit of uncertainty.
33:51 – Aravind Ratnam
I would say quite a bit of uncertainty in terms of timelines and who’s going to get there first and what are the use cases and all of that. With quantum sensing, there is no uncertainty. This physics has already been solved now. It’s turned into largely a problem of engineering. And we are one of the forerunners in the world, a few controls on forerunners in the world, because we have active partnerships with Lockheed Martin and Airbus and several others that we haven’t disclosed. And yeah, so it’s a matter of, look, commercial airliners need it. Defense needs it. How fast can we get it out onto ships and planes and whatever other vehicles need this technology? And yeah, this really is going to be the first real application of quantum.
34:38 – David W. Schropfer
That’s amazing. So you passed the point that you are comfortable, that you’ve proven the technology functions, and when you say it’s just an engineering problem, that means it’s just a matter of building the device-specific units that fit into an Airbus 300 or Lockheed Martin, whatever the latest gen is.
35:00 – Aravind Ratnam
So there’s going to be a bunch of platform integration that needs to get done. We need to make sure that the maps are robust. We need to make sure that the software is certified. We need to make sure that the sensor is hardened to a place where it’s nice and small and doesn’t, you know, it works at all times. And yeah, so engineering, not science.
35:20 – David W. Schropfer
So that’s the difference between a guy with a pick and an axe versus the theoretical physicist, right? And the engineering is the easy part, in other words, compared to coming up with a sensor that can pick up signals as minute as the ones we’ve been discussing.
35:38 – Aravind Ratnam
And the difference in our approach with other enterprising startups that are quite active in their marketing is we are silently executing. We’ve been contracted by DARPA to develop sensors that are, you know, an order of magnitude more sensitive and robust. And so we were the winner there. And yeah, so it’s really, really challenging because so many vehicles and platforms out there need us. But, you know, We are dealing with limited resources. But it’s absolutely fascinating. It’s a beautiful game of prioritization.
36:16 – David W. Schropfer
When you say that Q-Control was the winner in the DARPA contest, that win came with an award north of $20 million, if I’m not mistaken. Is that right?
36:27 – Unidentified Speaker
It’s Australian $38 million. I think it translates to US $24 million or so.
36:32 – David W. Schropfer
$24 million, OK. That’s not an easy win.
36:35 – Aravind Ratnam
Thank you. You’re welcome.
36:38 – David W. Schropfer
And it’s been wonderful having you on the show today. Where can people find out more about what you do?
36:45 – Aravind Ratnam
Yeah, so we maintain a very active website: https://q-ctrl.com/
And specifically go to a product called Ironstone Opal. That is really our quantum sensing product and on quantum computing, we have an education protocol, Black Opal. This is really cool. If you’re starting from scratch and you want to learn how to program quantum computers, please use Black Opal. It’s super cheap and there are more than 30,000 people in the world using it, including the government of the UK. That’s great. Then we have an EDA tool called Boulder Opal that’s used by dozens of customers across the world, super mature. If you’re building qubits, this is the tool for you. And then finally, our flagship error suppression product, which is called FireOpal, that debuted on IBM, but since then has grown to support numerous platforms. So yeah, we’re having a lot of fun doing all this.
37:47 – David W. Schropfer
That’s fantastic. And for my listeners, if you missed any of that, just go to diycyberguide.com, search for episode 8888, and see all the links that Aravind Ratnam just mentioned, all listed for your consumption. So Aravind Ratnam, wonderful to have you on the show. I really had a great time talking to you today.
38:06 – Aravind Ratnam
Thank you, David. It was a pleasure. Thank you.
DIY CyberGuy with David W. Schropfer 
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