Sebastian Hassinger • 00:02
I'm recording this episode at Quantum World Congress 2026, which means I'm surrounded by people who are, in various ways, trying to answer the same basic question: when does quantum computing stop being a research project and start being something you can actually use? Most of the conversations at an event like this focus on the hardware — qubit counts, error rates, the race to fault tolerance. But there's a whole other set of problems around commercial deployment that don't get nearly as much airtime, and they're the ones that tend to determine whether a technology actually lands in the world or just sits in a lab looking impressive.
My guest today, Isabella Bello Martinez, thinks about those problems for a living. She's a Senior Lead Quantum Technologist at Booz Allen Hamilton, where she leads quantum products and partnerships — which in practice means she's the person who has to figure out how quantum technology gets from wherever it is now to the hands of people who have real missions to accomplish. Her team works across quantum computing, quantum sensing, and post-quantum cryptography, which gives her an unusually wide view of where the field actually is versus claims usualy made in press releases.
What I find interesting about Isabella's background is that she didn't come up through physics. She's a biomedical engineer by training, and she did her master's work in engineering entrepreneurship — which included an economic analysis of how AI was going to reshape industry. She came to quantum not because she wanted to build qubits, but because she was drawn to the problem of building a workforce and an ecosystem around a technology that didn't yet have operational examples to point to. That's an unusual and specific thing to be drawn to, and I wanted to understand it.
We talk about what operationalization actually means — and why she thinks it's the step that comes after the business case, not before it. We get into quantum sensing, which is closer to real deployment than most people realize, and what happens when you try to put a precision navigation sensor in a waterproof box without destroying the thing that made it worth using. We talk about her hiring philosophy, which is deliberately and unapologetically interdisciplinary — and why she's more worried about the rise of pure quantum graduate programs than she is about the talent shortage everyone else is worried about.
And we talk about the moment her team sat down with a computational biologist and a computational chemist to walk through an actual drug discovery workflow — and discovered that the step everyone had assumed was the quantum opportunity wasn't the bottleneck at all. That story, to me, is the whole episode in miniature: the gap between what we think the problem is and what the problem actually is, and why closing that gap requires a very different kind of team than the field has traditionally tried to build.
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Sebastian Hassinger • 03:42
Thank you very much for joining me. I hope you've been having a good quantum world congress.
Isabella Martinez • 03:48
Yes, same.
Sebastian Hassinger • 03:49
so start with you know, your personal sort of journey into quantum computing as a as your as your profession, how did you get to where you are?
Isabella Martinez • 04:00
Yeah, I'm happy to talk about that. So my undergraduate degree is actually in biomedical engineering. And a really pivotal moment for me in shaping the rest of my career was that
I was doing an engineering capstone project where we were working directly with doctors at a hospital in order to improve some of the ostensibly biomedical devices that they were working with on a day-to-day basis. So for example, we had folks who were helping to design better sensors for detecting pulse for particular right kinds of skin tones. And the thing that we got assigned to after speaking to many, many nurses, we realized that the problem wasn't actually an engineering problem. An engineering tool existed for the problem they were having us solve.
Sebastian Hassinger • 04:43
Right.
Isabella Martinez • 04:44
The problem was that getting certified to use that tool took so long that most nurses didn't have the time to get trained on the tool.
Sebastian Hassinger • 04:52
Wow.
Isabella Martinez • 04:52
So instead of being an engineering problem, and we did come up with an engineering gadget because that was what was required for us to graduate. But what really stuck with me was that if I'd been in a different space and been able to take another angle, it was a training angle. you could argue that it was a culture angle because the fact that the nurses couldn't get the time That's not time off. They'd still be working, but couldn't get the time from their schedules to go and get trained in this life-saving tool was mind-boggling to me. And I thought this is the intersection I want to live at. I want to live at the intersection of engineering plus and back then I was like this must be engineering plus policy. and in order to
study that further and to study it at the master's level, I ended up doing a master's in engineering engineering entrepreneurship.
Sebastian Hassinger • 05:39
interesting.
Isabella Martinez • 05:40
And from there I did what ended up being an economic report on how AI was gonna affect industry. So not looking again into the actual development of AI algorithms, although I did that too because it was interesting. But my my thesis was actually on the impact it was going to have on how do we think about industry, the industry it was gonna create. And when I started working at Booz Allen, that was essentially what I started doing. It's like what is the industry that quantum computing is creating? And so I was brought into it as one of our technical writers initially, as someone who'd spent time thinking about the in the intersection of engineering and startups. I did a lot of work with startups as part of my master's thesis as well. And from there,
through a slightly winding path, I now have the distinct honor of being our quantum products and partnerships manager at Booz Allen. So My day-to-day work doesn't look too different from project management, except that the kind of technologies that I'm helping to manage projects for our focus on quantum computing and quantum algorithms.
Sebastian Hassinger • 06:45
Right.
Isabella Martinez • 06:45
And in particular, we've been talking a lot about not the algorithms that run today, but the algorithms that are going to be able to run on fault-tolerant architectures.
Booz Allen's been doing quantum computing research for more than ten years. A lot of people don't know that. Our post-quantum cryptography work is a bit newer than that, and our quantum sensing work is even newer than that. And the lens that we've always brought is that we are able to lean back on m many decades of
folks who are working in operational environments and be able to sit down with these people who have a really in-depth perspective on customer problems And ask them, what does it actually take to get this technology into the hands of the people who are gonna use them?
Sebastian Hassinger • 07:27
Right.
Isabella Martinez • 07:27
we're talking at Quantum World Congress, we just had the opening remarks of the startup pitch competition talked about the fact that first you have to solve for physics, then you have to solve for engineering, and then you have to solve for business case. But I would argue then you have to solve for operationalizing something.
Sebastian Hassinger • 07:46
Right.
Isabella Martinez • 07:47
Which is even beyond that.
Sebastian Hassinger • 07:48
Yeah.
Isabella Martinez • 07:48
And all of that requires very different workforce elements. And that is what gets me really excited is what does it mean to be an interdisciplinary team right that can draw on the right elements of the right part of the workforce at the right time. And how are you an ecosystem kind of connector Because it is very difficult to find all those different elements and all those different backgrounds in one place. And while I think that my team does it pretty well, we also do a good job because we have a very broad a range of partners who we bring in at different steps when the customers are interested in different steps around the journey because there are many customers that are interested in fundamental research and development. And the mix of who you bring with you changes as we get further along this commercialization curve.
Sebastian Hassinger • 08:35
That's so interesting. I mean, your capstone, the insight there was around an existing
solution that just wasn't didn't have a path to adoption. And then you got into AI impact, which is still playing out right now in very real time. And still has a lot of question marks about it. But then in Booz Allen, your role is looking even further, you know, further horizon kind of I mean you know, the what's the impact of quantum right now? Well, we're all kind of puzzled about it. That's kind of the impact about it. And then how do you how do you project that far into the future to try to work out I mean at this point are you the challenge is it more about s getting, you know, working with your customers to start to grapple with understanding how Quantum is evolving and what problems it might be applicable for, or what what's the sort of typical engagement right now?
Isabella Martinez • 09:28
That's a really good question. And maybe to take a step back and answer one of your earlier ones. The reason that I was interested in, you know, skipping over AI, if you will, and becoming engaged and working on a quantum computing team was that
I was really interested in this problem being tackled for the first time. And something that they really like to say in the AI industry is that it's been along, a lot longer than people realize because folks were theorizing about AI so early. One can argue quantum computing has been around for even longer then. But no one actually thought about the practical realities of how do you build a workforce in the absence of operational technology. And I just found that to be the kind of challenge that I get gets me very excited. And
the good thing about working in a technology space that it has a lot of really strong mathematical and
physics work behind it is that we are able to point to the, you know, this problem space that we can't a attack with classical computing and or we don't believe we're going to be able to with AI models, even though they do continue to, I think, surprise everyone, even those who work in the field. And we can say there's a class of problem that we really think we can only tackle with quantum computing.
And
One of the reasons that my team works in quantum sensing and PQC as well as quantum computing is that right now we're beginning to grapple with the adoption of quantum sensors. I can go and order a quantum sensor online.
Sebastian Hassinger • 11:01
Right.
Isabella Martinez • 11:01
Which is something that I think would have been mind-boggling to folks twenty years ago.
Sebastian Hassinger • 11:06
Yeah.
Isabella Martinez • 11:06
And
You know, we're not ready to order quantum computing quantum computers online.
Sebastian Hassinger • 11:11
Not quite.
Isabella Martinez • 11:12
But some of the system integration problems and the conversations that we are having with the folks where we who
are well versed in the platforms that we're trying to put the quantum sensors on, the kind of engineering problems that we're having with them and the tack tackling things like, you know, how do these things connect in a physical sense
seed the ground for the kind of conversations we're going to have in the quantum computing industry. Except rather than thinking about putting a quantum computer on a moving platform, we're probably talking to data center providers and thinking How are we connecting it into your data center?
we're doing a wide variety, but one that we've recently released some work on is just looking into different quantum sensors that could help with Assured position navigation and timing.
Sebastian Hassinger • 12:05
Yeah, okay. Yeah, yeah.
Isabella Martinez • 12:07
And more to come on that soon.
Sebastian Hassinger • 12:08
Okay, great. Yeah. I find that whole element of B PNT very, very interesting. And you're right, it's it feels like it's imminent. I mean the technology is very ready for deployment and working through exactly what you're talking about of like What's the problem space? Where does it need to be mount, you know, mounting integration, like and then the workforce development.
Isabella Martinez • 12:30
Well we were talking to one vendor, for example, who said, Oh, by the way, this can't get wet.
Well
Sebastian Hassinger • 12:38
good
Isabella Martinez • 12:39
news for the Air Force, I guess. I'm like, all right, so that limits what I can do with it. You know, then I go to people who spend their life thinking.
I was like, could we
work with another partner or even in our own prototyping shops, should could we build the enclosure for it?
And then that becomes a really interesting mix of skill sets because they're talking to my team about you know, what is going to be the impact to the quantum sensor if we add all of these additional requirements and, you know, literally a physical enclosure for this particular device. How is that going to change the sensitivity?
Sebastian Hassinger • 13:14
Right.
Isabella Martinez • 13:14
Is it now worth it from the perspective of the end customer? If we've put this literally in a box, in a waterproof box, has it lost all of the extra precision that we were hoping it was going to give us? Right. And those are I think really exciting problems to tackle and they require bringing in people with a di
different set of skill sets from one another.
Sebastian Hassinger • 13:34
Right.
Isabella Martinez • 13:35
The people who spend all of their time thinking about, you know, the error tolerance of these quantum sensors are different from folks who are thinking about, okay, but this is going to be underwater or this is going to f be flying in a hurricane to collect meteorological data. This is going to be in space being bombarded from radiation from the sun without protection from the atmosphere. And these are all different people who often have had very different professional experiences and all of us have the same title of engineer.
Sebastian Hassinger • 14:01
Right, right. Yeah. Is that an example that sort of ruggedizing an existing sensing technology, is that how you would define a product in your kind of
Or does does it the a product in your context does that need to have a specific solution, like more specific than just ruggedizing a sensing device?
Isabella Martinez • 14:24
That is a very good question. Let me think about that for a second. I haven't thought about that angle before.
Isabella Martinez • 14:46
Now I found me like you need to do a follow-up with me in a couple of months. Okay. So the way that we are defining products tends to look a little different depending on the customer that we're working with.
Sebastian Hassinger • 14:56
Yeah. And that's kind of what I'm digging at is exactly that. Yeah. Different engagement models.
Isabella Martinez • 15:01
Exactly. And I
think within the quantum sensing space. we haven't seen the desire for a physical product grow.
There is a sense
that
quantum sensing companies or particular quantum sensors even are graduating from programs such as those that have been run s very successfully, I might add, by DARPA for many years.
Sebastian Hassinger • 15:25
Right.
Isabella Martinez • 15:26
And now that they've graduated very similar to graduating from a startup incubator, people just want to go and buy.
Sebastian Hassinger • 15:33
Right, right, right, right.
Isabella Martinez • 15:34
And the
Sebastian Hassinger • 15:36
capstone project.
Isabella Martinez • 15:37
Exactly. We're taking them through the capstone project. So it
needs to come with a lot more considerations and sometimes the original founders, original creators of these devices have thought of
Sebastian Hassinger • 15:48
well and in some cases they're just trying to create a device that's a supply chain component, right? I mean they're they're not necessarily thing I mean, a whole product takes in the context of the customers you're trying to address and they're all all the things you mentioned in your capstone project context. And that's not necessarily what somebody is making. a really exotic PNT quantum device needs to be putting their time into so it's almost like a translation f function in that case.
Isabella Martinez • 16:17
Yeah and it's several layers of translations because we Have also had a lot of really interesting conversations with our our network of collaborators around there's a translation
to other entities that are used to being supply chain components. So even the conversations that the creator of the quantum sensor, for example, is having with all of the different entities that they work with to source their own components. And then it's a translation to the systems integrator that's going to take the quantum sensor and to put it on into the space of interest. And then
I think quantum this is where quantum sensing and quantum computing begin to diverge from one another because in the ideal scenario, someone who is operating, let's say, a drone It doesn't need to necessarily know that the data that they're getting is quantum data versus the data they're getting is classical data. From the perspective of an operator, it's the same.
Sebastian Hassinger • 17:12
Right.
It's more accurate or it's more robust, or it's but it has some kind of classical attribute that presents to them as just better data.
Isabella Martinez • 17:22
Exactly. They haven't changed the way that they're interacting with the drone.
going to happen with quantum computing is at least for a time there will probably be a change in the way that people are operating are interacting with the data or with the problem. And that I think is an additional place where having an interdisciplinary team and an interdisciplinary background is very important.
Sebastian Hassinger • 17:44
Right.
Isabella Martinez • 17:44
Because there's the designing of an algorithm to make it going back to the you know, four steps that I mentioned earlier.
Sebastian Hassinger • 17:51
Right.
Isabella Martinez • 17:51
You know, there is a designing an algorithm that is sound in the sense that the quantum physics checks out.
Sebastian Hassinger • 17:56
Yes.
Isabella Martinez • 17:57
And that is sound in the sense that the algorithm runs successfully. And let's say let's make it a little harder and say that it's an optimized for the particular combat hardware and software stack that's been chosen. And
a huge industry in and of itself right now. And I think I left before our our software. Startups could finish presenting at the pitch competition, but you know
Sebastian Hassinger • 18:20
Sorry to take you away.
Isabella Martinez • 18:21
Yeah, no worries, no worries. but
But there's also and let's say that then you have to work with someone to make sure that the algorithm is being applied to a problem that has monetary value, which is the big
Sebastian Hassinger • 18:35
And in an accurate way. I mean if it's if it's if it's let's say it's in life science or bioscience or whatever, then you need expertise to almost match the physics expertise to make sure that translation is happening accurately. Exactly.
Isabella Martinez • 18:48
And that, you know, it's actually commercially viable. Is the is the extra precision or efficiency or, you know, whichever of the chosen directions one has gone with designing this algorithm, is it
Worse it?
Sebastian Hassinger • 19:03
Right.
Isabella Martinez • 19:03
How many times do you need to run it?
Sebastian Hassinger • 19:05
Right.
Isabella Martinez • 19:05
Who is running it? Who are you paying?
Sebastian Hassinger • 19:07
The economics of it. Exactly.
Isabella Martinez • 19:10
And then there's also, you know And who's gonna buy it?
Sebastian Hassinger • 19:14
Yeah.
Isabella Martinez • 19:14
And who's gonna buy it also is driven by who's actually using it?
Sebastian Hassinger • 19:18
Yeah.
Isabella Martinez • 19:18
Are these computational biologists who are already used to adopting cutting edge tools into their workflows?
Sebastian Hassinger • 19:24
Right.
Isabella Martinez • 19:24
Is this people who
have mostly spent their careers optimizing things, you know, f in databases, in spreadsheets.
Sebastian Hassinger • 19:34
Right.
Isabella Martinez • 19:35
And now we're telling them to adopt the quantum computer.
Sebastian Hassinger • 19:37
Yeah.
Isabella Martinez • 19:38
And I know that there is a there's a desire in the field to make this as simple as a command line prompt. And there's so many steps to getting from here to there. And, you know, is the u does the user want a command line prompt? Is that what the user wants?
Sebastian Hassinger • 19:52
Right, right, right. Right. And so at this stage, I mean,
That whole exploration, I mean, i many enterprises that I've seen and you know, companies, you know, whether it was I've been at IBM or AWS or partners that I've worked with. they're engaging enterprises and they're taking them down an explo exploratory path. It almost it almost doesn't matter where you start in terms of a use case 'cause the Job one is sort of let's just figure out like help you figure out what quantum is and start to develop a quantum intuition, if you will Is that part of what you're doing in your role and the team that you lead? Or are you more at the stage of like, once you understand as a customer, where you want to apply a quantum, we're gonna help with the application, the deployment.
Isabella Martinez • 20:43
We are able to do both. And I think that the amount that we are doing one over the other
has changed a lot over the years. right now we've been really focused on more education around post quantum cryptography just because of the situation that we're in.
Sebastian Hassinger • 20:59
Yes.
Isabella Martinez • 21:00
with, you know, new
policy and laws coming out about that adoption.
Sebastian Hassinger • 21:05
And the compression of the compression of the timescale.
Isabella Martinez • 21:08
And that conversation, that education, we really don't need to
Sebastian Hassinger • 21:12
It
Isabella Martinez • 21:13
spend so much time talking about quantum computing because th the cybersecurity industry understands that cyber threats are evolving.
Sebastian Hassinger • 21:20
Exactly. It fits into the threat profile, it's just a new one.
Isabella Martinez • 21:23
Exactly. And when you're talking to somehow adopting quantum sensing You all they care about is how much more precise?
Sebastian Hassinger • 21:29
Right.
Isabella Martinez • 21:30
How much is it gonna cost me? What is the operational imp like how much slower is the platform now that it's got this extra weight on top? Size, weight, power, cost. Right With quantum computing, I think we do still have an amount of education to do, but I actually I dare say that the quantum community has actually done a really good job, including certain resources like your book and your podcast.
I actually
I'm so surprised that when I tell people that I work in quantum computing, people know what I'm talking about. They might not be completely a hundred
Sebastian Hassinger • 22:03
changed
four years ago.
Isabella Martinez • 22:04
Exactly. And it like through the course of my career has been from I don't really feel like explaining to in this random networking party that I'm here with my spouse, I don't feel like explaining my job. I'm just gonna say I'm a computer engineer and hope I don't land myself in hot water to saying, oh, I am a project manager and lead partnerships for a quantum computing team and people know what I mean.
Sebastian Hassinger • 22:24
Yeah.
Isabella Martinez • 22:25
And that's very surprising. So it's more the education of how is quantum computing different from AI? Why is there space for both in the market? So that once again goes down to use case.
Sebastian Hassinger • 22:37
Yeah.
Isabella Martinez • 22:38
And it's ver it's always been very difficult to explain quantum technologies without using some analogies. But because we can now point to discrete use cases and tell someone, you know, quantum computing is not going to be optimizing your Netflix algorithm.
Sebastian Hassinger • 22:51
Right. Yeah, yeah.
Isabella Martinez • 22:52
it's instead going to be used to do really high-end
molecular simulation for pharmaceutical industry, people go, Oh, that makes sense.
And
as you said, that wasn't the case a few years ago. And the same is true with our customers, is that they're coming to us more now and saying
When am I getting this? Right. And when can it do this optimization use case that I heard it can do? Or when is it going to be able to do high speed training or what is it going to be able to actually plug in to help with the understanding of a biological component for all sorts of modeling and simulation use cases.
And
I think the AI bubble is both giving urgency to the question and also people are are like, okay, we're gonna wait and see when things are ready, because if we jump in too soon, suddenly there's gonna be this big leap in capability. And it's like, oh, all the things that we said were AI before is nothing compared to what we're saying AI now. And I think there's a little bit of that coming in quantum as well.
Sebastian Hassinger • 23:57
Interesting. Interesting. Is there
Do you see any particular
sort of trends or groupings in the types of customers, the industries or the use cases that are are coming to at this stage?
Isabella Martinez • 24:11
I don't know if I can answer that right now.
Sebastian Hassinger • 24:13
Okay. Fair. let's see. So okay. I if you're talking about
engaging in that's fine. I'll wait all the way to
Isabella Martinez • 24:25
I'm
I have not lived in the DMV for many years and I'm now allergic to something. So I apologize. Probably everything. Thank you.
Sebastian Hassinger • 24:34
I live in Austin, so I'm familiar with being allergic to everything.
Isabella Martinez • 24:37
Yeah.
All right, I'm yours again. Thank you.
Sebastian Hassinger • 24:45
okay, so when you're as you said, the question is now when. So when you engage with a customer like that, are you Are you diving right into use case definition, algorithm exploration, resource estimation? Or are you starting Are you still having to start with sort of like earlier stage discussions of like talking through what they think it's gonna be good for versus what it might actually be good for, those types of calibration conversations?
Isabella Martinez • 25:14
I promise that I'm not ducking the question. It's genuinely a case-by-case situation. I it is a little bit of the Wild West where we're getting some
you know, very clear use cases and very clear requirements and a very clear yes or no.
Sebastian Hassinger • 25:32
Right.
Isabella Martinez • 25:32
And there's other
areas where we get to explore and play really a little bit more.
Sebastian Hassinger • 25:38
Yeah. And in terms of workforce development, are you
sort of I imagine you're you've got sort of a strategy for building up your own internal resources. Are you also sort of advising and reflecting that strategy to your customers and sort of telling them
how to build those kind of interdisciplinary teams to address this space?
Isabella Martinez • 26:00
I think one of the ways that we do that is by being very clear why we're bringing the kinds of people that we bring in our team in a given situation. I, for example, at one point in time, we had some we had two folks on our team, one who was trained in chemistry and one who was trained in biology. And we sat down with a couple of folks trained in quantum physics and someone who's training who's doing theoretical quantum algorithm work because they were again building algorithms for fault tolerance that this was a few years ago, so d d definitely felt like it was far away. And it was by sitting down and talking through the actual workflow of a biologist and a chemist sitting down to do the very beginning steps of I believe it was a health use case and I believe it had to do with the generation of syn synthetic molecules in order to see if they were good candidates for a, you know, drug of interest. And they sat us down through how they actually did that. And the part of the problem that the folks with the physics background thought was gonna be better suited for a quantum computer turned out to not be the computational battle bottleneck. So even if a quantum computer were going to save time or energy on that step it didn't really matter to the computational biologist and the computational chemist who'd be actually using, you know, this theoretical tool we're we're talking through. It was in a completely different part of the problem. And we wouldn't have known that without sitting down and having several hours of deep conversation about the work, like the steps that they take as people with lab bench experience who then became computational. bi computational biologist and chemist. And that was such an eye-opening experience for me and it happened, you know, fairly er fairly early in my managerial career of being like, I need to do this purposefully. I have to purposefully open reqs where we are looking for people with more than just physics degree. We're looking for people Who have interests and have explored things reflected in internships or extracurricular activities or
you know, they've gone off and done something else for a little bit and then they've come back to physics and to quantum. Those are the people who we are looking for and those are the people who we are then bringing to our client spaces with and we're telling our clients this is why they're here. They're here because they've had this unique experience.
Sebastian Hassinger • 28:30
Right. And are those all sort of
advanced degree, PhD level degrees in physical sciences, is that sort of the main skills that you're you're needing in your team or is it is it broader than that?
Isabella Martinez • 28:44
It's much broader than that. we have folks with criminology degrees, we have folks with statistics and math and as you said, the hard sciences, also economist, psychologist. I'm a biomedical engineer. I'm not the only person who has an engineering degree. but also people who've, you know, gone off and like founded a data science company. someone who went and like founded an education startup and then came back and was a data scientist for a little bit and now is doing quantum. So just folks who really have a
a penchant for creativity.
Sebastian Hassinger • 29:17
Yeah.
Isabella Martinez • 29:18
And I think we need more of that. And I my undergraduate institution was a liberal arts college. and that sometimes surprises people to hear
Sebastian Hassinger • 29:27
I have a B in English, just for the records.
Isabella Martinez • 29:29
Yeah, no, amazing. This is exactly what I'm speaking to. No, like we need the just having had
whether formal or informal, immersion in something else. I think, particularly since quantum computing is so new and By definition, we're telling people that you are decomposing a problem and rebuilding it from scratch, that you are approaching the end use case in a different way. from classical computing. And so I'm always so thrilled there seem to be a lot of people in the quantum industry who are also musicians. Like everyone this is bringing just Skills and experiences from different fields. And I think that's one of the things that makes the industry so
fun And I think it's also something that needs to be more purposeful in the way that we recruit and also the kinds of work that the quantum economy is trying to support and the kinds of programs that we're trying to support in education. you and I talked about before we started recording about a lot of work that universities do and workforce development, but
the kinds of people who are being hired, I think shapes workforce development a lot more than any university program ever could because you can be a world class university program, but if no one's hiring your graduates Eventually it you know, you might continue to have graduates, but they're going to struggle and eventually it will move someplace else. Because that's not serving the goals of the university either if the graduates aren't being hired. And we're starting to see all of these masters in quantum algorithms development, which makes me excited.
Sebastian Hassinger • 31:08
Quantum engineering
Isabella Martinez • 31:08
too. Exactly. But it also worries me Because then there are people who've only done quantum. And the way that there were people who only did computer science and we found that they weren't bringing that creative spark to the spaces that they were in.
Sebastian Hassinger • 31:21
So if you could wave a wand and like change the education landscape, then I'm assuming you would before you sort of increase the volume of, let's say, physics PhDs or other physical science PhDs, you'd You'd want more interdisciplinary, kind of purposefully interdisciplinary programs.
Isabella Martinez • 31:39
Yes.
Sebastian Hassinger • 31:39
Yeah. Interesting.
Isabella Martinez • 31:40
And whether it's the individuals who are interdisciplinary
or
Interdisciplinary requirements.
there is some work, I forget who published it, it was a few years ago, where
It's like if you are an undergraduate with a computer science degree, but you are forced to take classes with let's say historians and artists and all of you have to do a project together, one also learns how to communicate. And I think, you know, much lower stakes when you're a sophomore in college.
But those skills are brought with you into your career. And those experien we're all made up of the sum of all of our
experiences. So that's what I would like. Either when you graduate there's some requirement for having taken classes other than than physics and computer science or you've been required to work very purposefully with someone who has a completely different interest than you do.
Sebastian Hassinger • 32:39
Right.
Isabella Martinez • 32:39
And hopefully they've both learned something. And maybe they go then write a book about quantum.
Sebastian Hassinger • 32:45
Yeah. So okay, so coming back to, you know Sensing is sort of you know on the verge of launching in various forms in terms of commercialization, deployment, production deployment.
Isabella Martinez • 32:57
Exactly.
Sebastian Hassinger • 32:58
PQC
is very much surging in terms of commercialization, production, deployment.
Isabella Martinez • 33:05
And I would say it's in a very similar space because I think something that we've seen this year is existing
organizations that have very strong cyber products and have been leading in the classical cyberspace have all
put out PQC versions of their tools. So I think that's making a very big difference.
Sebastian Hassinger • 33:24
Me too. So okay, so those two are sort of, you know, on the cusp of turning into viable, you know, real markets.
Isabella Martinez • 33:32
Right.
Sebastian Hassinger • 33:32
quantum computing is still you know, i nascent, I would say. Do you think there will be
products, customer solutions, things that you deploy for customers, with customers, with partners? i pre-fault tolerant quantum computing? Do you think NISQ is gonna be useful for anything in the near term, or are you sort of looking at you know, twenty thirty, let's say, and beyond when when f actual logical qubits are are shipping, and we're starting to see fault tolerant convenience scale.
Isabella Martinez • 34:08
I think
that we have seen evidence that really good scientific output is happening with the NIST devices. And there is I absolutely believe that there is value in science for science's sake as well. And I had actually meant to look it up before talking to you, but I think we can remove this if I'm wrong. I think it was
Someone used a NIST device to characterize a black hole. It was like wildly better than what they could have done classically.
Sebastian Hassinger • 34:41
Okay, I think which was related in some way to the black hole. I believe, if I'm not mistaken. But yeah, he did he did it on an IBM device, I think he used 103 qubits or something like that, and he did his own custom error mitigation and he did get, I mean it was a it was a better result than he would have gotten classic.
Isabella Martinez • 35:00
Right.
Sebastian Hassinger • 35:00
At least the way you described it to me. Okay. That may be what you're thinking.
Isabella Martinez • 35:04
Yeah, I think that's what I'm thinking of.
Sebastian Hassinger • 35:06
And I agree with you. I think there's there's definitely You know, I mean they are scientific instruments. We're getting real science.
Isabella Martinez • 35:13
Exactly. And I think that there is value to the scientific community in the science that's been done.
And that will continue to be of value regardless of where the industry goes. Now, I am a believer that we are indeed heading into an era of fault tolerance and we will get there and the nature of those products are going to change.
I think that
groups that are and it segments of industry that are reliant on RD, such as pharmaceutical, are going to
see those benefits in a very real way, but maybe not in a way that is
apparent
to
to someone who's not in the quantum industry, let's say. And
I think that'll be I do not feel very confident in my predictions of what that's gonna look like, but I think the early twenty thirties are gonna be very interesting for all of us.
Sebastian Hassinger • 36:08
Yeah, I agree. I agree. And I mean Going back to what you were saying about the I mean, for one thing, the fact you're attracted to the space because there aren't answers to these questions I'm very much the same. It's what it's what keeps me super engaged. And I also think that if you're collecting a group of people on your team that are interdisciplinary and also are drawn to those hard problems for which answers aren't don't preexist. There's nothing you can sort of draw on. You're much more likely to be the first people to hit on. really creative solutions to problems and applications of the technology. So I think you're taking the right approach.
Isabella Martinez • 36:48
Thank you. So that's the hope. And I think that no matter what, one of the things that was a big theme of Quantum World Congress last year was the need for sure, you know, everyone with all these different startups are competing for the same limited a pool of investment funding and the same you know, limited early small business research opportunities. But
we can't all tackle everything at once. And it does take a network. And I think approaching
not only with a mindset of creativity, but also a mindset
of
how do we work together to be creative and how do we draw one another's creativity in order to create something entirely new I'm really hoping that we don't lose that collaborative spirit as we enter into this new computation computational era that we're all helping to create.
Sebastian Hassinger • 37:41
New quantum era, you might say, is
Isabella Martinez • 37:46
I was trying to very hard to not say that.
Sebastian Hassinger • 37:50
I need the plug.
Isabella Martinez • 37:54
Oh, I forgot I'm sorry. I forgot that was the name of the podcast.
All right, right, wait, okay, okay. Let me resay that.
Sebastian Hassinger • 38:05
Okay.
Isabella Martinez • 38:06
Yeah.
I really hope that we don't lose that mindset of how all of us being creative together and working as a as a group and being collaborative is what's allowing us to enter this new quantum era.
Sebastian Hassinger • 38:18
Thank you for the shameless plug. It's a good name for a podcast.
Isabella Martinez • 38:23
It's an absolutely good name
Sebastian Hassinger • 38:25
I agree. Well, thank you very much, Isabel. This has been very interesting. I really do think it's it's it's exciting. The team you're putting together and what you're the way that you're tackling in the problem space is really exciting. So I look forward to seeing what you do. So
Isabella Martinez • 38:40
Thank you.
Sebastian Hassinger • 38:42
Thanks so much to Isabella Martinez for taking the time to sit down with me at Quantum World Congress — and for being so candid about what the work actually looks like from the inside.
The thing that's going to stay with me from this conversation is that capstone project story from her undergraduate years — the one where the engineering solution already existed, and the real problem was that nurses couldn't get time off to be trained on it. She's been chasing that intersection ever since: the gap between a working technology and a technology that actually works for the people who need it. I think that framing is one of the most useful things I've heard articulated about where quantum is right now, across all three of its major near-term vectors.
Show notes for this episode are at newquantumera.com, where you'll find links to Isabella's published work on PQC migration and quantum workforce strategy, background on Booz Allen's quantum practice, and related policy context including the NIST NCCoE's PQC migration resources. If you want to go deeper on the workforce and careers side of quantum, I'd point you to our episodes with Anastasia Marchenkova, Deeya Viradia, and Piotr Lewandowski — all linked in the show notes.
If you're not already subscribed to the newsletter at newquantumera.com, that's the best way to stay current on new episodes and the ideas we're tracking between them.
And if this episode was useful to you — if it gave you a new frame for thinking about operationalization, or workforce, or just the honest state of quantum deployment — the best thing you can do is share it with someone who's working on the same questions. Word of mouth is genuinely how this show grows, and I'm grateful for every recommendation.
Thanks for listening. I'm Sebastian Hassinger, and this has been The New Quantum Era. Theme music by OCH. See you next time.
I'm recording this episode at Quantum World Congress 2026, which means I'm surrounded by people who are, in various ways, trying to answer the same basic question: when does quantum computing stop being a research project and start being something you can actually use? Most of the conversations at an event like this focus on the hardware — qubit counts, error rates, the race to fault tolerance. But there's a whole other set of problems around commercial deployment that don't get nearly as much airtime, and they're the ones that tend to determine whether a technology actually lands in the world or just sits in a lab looking impressive.
My guest today, Isabella Bello Martinez, thinks about those problems for a living. She's a Senior Lead Quantum Technologist at Booz Allen Hamilton, where she leads quantum products and partnerships — which in practice means she's the person who has to figure out how quantum technology gets from wherever it is now to the hands of people who have real missions to accomplish. Her team works across quantum computing, quantum sensing, and post-quantum cryptography, which gives her an unusually wide view of where the field actually is versus claims usualy made in press releases.
What I find interesting about Isabella's background is that she didn't come up through physics. She's a biomedical engineer by training, and she did her master's work in engineering entrepreneurship — which included an economic analysis of how AI was going to reshape industry. She came to quantum not because she wanted to build qubits, but because she was drawn to the problem of building a workforce and an ecosystem around a technology that didn't yet have operational examples to point to. That's an unusual and specific thing to be drawn to, and I wanted to understand it.
We talk about what operationalization actually means — and why she thinks it's the step that comes after the business case, not before it. We get into quantum sensing, which is closer to real deployment than most people realize, and what happens when you try to put a precision navigation sensor in a waterproof box without destroying the thing that made it worth using. We talk about her hiring philosophy, which is deliberately and unapologetically interdisciplinary — and why she's more worried about the rise of pure quantum graduate programs than she is about the talent shortage everyone else is worried about.
And we talk about the moment her team sat down with a computational biologist and a computational chemist to walk through an actual drug discovery workflow — and discovered that the step everyone had assumed was the quantum opportunity wasn't the bottleneck at all. That story, to me, is the whole episode in miniature: the gap between what we think the problem is and what the problem actually is, and why closing that gap requires a very different kind of team than the field has traditionally tried to build.
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Sebastian Hassinger • 03:42
Thank you very much for joining me. I hope you've been having a good quantum world congress.
Isabella Martinez • 03:48
Yes, same.
Sebastian Hassinger • 03:49
so start with you know, your personal sort of journey into quantum computing as a as your as your profession, how did you get to where you are?
Isabella Martinez • 04:00
Yeah, I'm happy to talk about that. So my undergraduate degree is actually in biomedical engineering. And a really pivotal moment for me in shaping the rest of my career was that
I was doing an engineering capstone project where we were working directly with doctors at a hospital in order to improve some of the ostensibly biomedical devices that they were working with on a day-to-day basis. So for example, we had folks who were helping to design better sensors for detecting pulse for particular right kinds of skin tones. And the thing that we got assigned to after speaking to many, many nurses, we realized that the problem wasn't actually an engineering problem. An engineering tool existed for the problem they were having us solve.
Sebastian Hassinger • 04:43
Right.
Isabella Martinez • 04:44
The problem was that getting certified to use that tool took so long that most nurses didn't have the time to get trained on the tool.
Sebastian Hassinger • 04:52
Wow.
Isabella Martinez • 04:52
So instead of being an engineering problem, and we did come up with an engineering gadget because that was what was required for us to graduate. But what really stuck with me was that if I'd been in a different space and been able to take another angle, it was a training angle. you could argue that it was a culture angle because the fact that the nurses couldn't get the time That's not time off. They'd still be working, but couldn't get the time from their schedules to go and get trained in this life-saving tool was mind-boggling to me. And I thought this is the intersection I want to live at. I want to live at the intersection of engineering plus and back then I was like this must be engineering plus policy. and in order to
study that further and to study it at the master's level, I ended up doing a master's in engineering engineering entrepreneurship.
Sebastian Hassinger • 05:39
interesting.
Isabella Martinez • 05:40
And from there I did what ended up being an economic report on how AI was gonna affect industry. So not looking again into the actual development of AI algorithms, although I did that too because it was interesting. But my my thesis was actually on the impact it was going to have on how do we think about industry, the industry it was gonna create. And when I started working at Booz Allen, that was essentially what I started doing. It's like what is the industry that quantum computing is creating? And so I was brought into it as one of our technical writers initially, as someone who'd spent time thinking about the in the intersection of engineering and startups. I did a lot of work with startups as part of my master's thesis as well. And from there,
through a slightly winding path, I now have the distinct honor of being our quantum products and partnerships manager at Booz Allen. So My day-to-day work doesn't look too different from project management, except that the kind of technologies that I'm helping to manage projects for our focus on quantum computing and quantum algorithms.
Sebastian Hassinger • 06:45
Right.
Isabella Martinez • 06:45
And in particular, we've been talking a lot about not the algorithms that run today, but the algorithms that are going to be able to run on fault-tolerant architectures.
Booz Allen's been doing quantum computing research for more than ten years. A lot of people don't know that. Our post-quantum cryptography work is a bit newer than that, and our quantum sensing work is even newer than that. And the lens that we've always brought is that we are able to lean back on m many decades of
folks who are working in operational environments and be able to sit down with these people who have a really in-depth perspective on customer problems And ask them, what does it actually take to get this technology into the hands of the people who are gonna use them?
Sebastian Hassinger • 07:27
Right.
Isabella Martinez • 07:27
we're talking at Quantum World Congress, we just had the opening remarks of the startup pitch competition talked about the fact that first you have to solve for physics, then you have to solve for engineering, and then you have to solve for business case. But I would argue then you have to solve for operationalizing something.
Sebastian Hassinger • 07:46
Right.
Isabella Martinez • 07:47
Which is even beyond that.
Sebastian Hassinger • 07:48
Yeah.
Isabella Martinez • 07:48
And all of that requires very different workforce elements. And that is what gets me really excited is what does it mean to be an interdisciplinary team right that can draw on the right elements of the right part of the workforce at the right time. And how are you an ecosystem kind of connector Because it is very difficult to find all those different elements and all those different backgrounds in one place. And while I think that my team does it pretty well, we also do a good job because we have a very broad a range of partners who we bring in at different steps when the customers are interested in different steps around the journey because there are many customers that are interested in fundamental research and development. And the mix of who you bring with you changes as we get further along this commercialization curve.
Sebastian Hassinger • 08:35
That's so interesting. I mean, your capstone, the insight there was around an existing
solution that just wasn't didn't have a path to adoption. And then you got into AI impact, which is still playing out right now in very real time. And still has a lot of question marks about it. But then in Booz Allen, your role is looking even further, you know, further horizon kind of I mean you know, the what's the impact of quantum right now? Well, we're all kind of puzzled about it. That's kind of the impact about it. And then how do you how do you project that far into the future to try to work out I mean at this point are you the challenge is it more about s getting, you know, working with your customers to start to grapple with understanding how Quantum is evolving and what problems it might be applicable for, or what what's the sort of typical engagement right now?
Isabella Martinez • 09:28
That's a really good question. And maybe to take a step back and answer one of your earlier ones. The reason that I was interested in, you know, skipping over AI, if you will, and becoming engaged and working on a quantum computing team was that
I was really interested in this problem being tackled for the first time. And something that they really like to say in the AI industry is that it's been along, a lot longer than people realize because folks were theorizing about AI so early. One can argue quantum computing has been around for even longer then. But no one actually thought about the practical realities of how do you build a workforce in the absence of operational technology. And I just found that to be the kind of challenge that I get gets me very excited. And
the good thing about working in a technology space that it has a lot of really strong mathematical and
physics work behind it is that we are able to point to the, you know, this problem space that we can't a attack with classical computing and or we don't believe we're going to be able to with AI models, even though they do continue to, I think, surprise everyone, even those who work in the field. And we can say there's a class of problem that we really think we can only tackle with quantum computing.
And
One of the reasons that my team works in quantum sensing and PQC as well as quantum computing is that right now we're beginning to grapple with the adoption of quantum sensors. I can go and order a quantum sensor online.
Sebastian Hassinger • 11:01
Right.
Isabella Martinez • 11:01
Which is something that I think would have been mind-boggling to folks twenty years ago.
Sebastian Hassinger • 11:06
Yeah.
Isabella Martinez • 11:06
And
You know, we're not ready to order quantum computing quantum computers online.
Sebastian Hassinger • 11:11
Not quite.
Isabella Martinez • 11:12
But some of the system integration problems and the conversations that we are having with the folks where we who
are well versed in the platforms that we're trying to put the quantum sensors on, the kind of engineering problems that we're having with them and the tack tackling things like, you know, how do these things connect in a physical sense
seed the ground for the kind of conversations we're going to have in the quantum computing industry. Except rather than thinking about putting a quantum computer on a moving platform, we're probably talking to data center providers and thinking How are we connecting it into your data center?
we're doing a wide variety, but one that we've recently released some work on is just looking into different quantum sensors that could help with Assured position navigation and timing.
Sebastian Hassinger • 12:05
Yeah, okay. Yeah, yeah.
Isabella Martinez • 12:07
And more to come on that soon.
Sebastian Hassinger • 12:08
Okay, great. Yeah. I find that whole element of B PNT very, very interesting. And you're right, it's it feels like it's imminent. I mean the technology is very ready for deployment and working through exactly what you're talking about of like What's the problem space? Where does it need to be mount, you know, mounting integration, like and then the workforce development.
Isabella Martinez • 12:30
Well we were talking to one vendor, for example, who said, Oh, by the way, this can't get wet.
Well
Sebastian Hassinger • 12:38
good
Isabella Martinez • 12:39
news for the Air Force, I guess. I'm like, all right, so that limits what I can do with it. You know, then I go to people who spend their life thinking.
I was like, could we
work with another partner or even in our own prototyping shops, should could we build the enclosure for it?
And then that becomes a really interesting mix of skill sets because they're talking to my team about you know, what is going to be the impact to the quantum sensor if we add all of these additional requirements and, you know, literally a physical enclosure for this particular device. How is that going to change the sensitivity?
Sebastian Hassinger • 13:14
Right.
Isabella Martinez • 13:14
Is it now worth it from the perspective of the end customer? If we've put this literally in a box, in a waterproof box, has it lost all of the extra precision that we were hoping it was going to give us? Right. And those are I think really exciting problems to tackle and they require bringing in people with a di
different set of skill sets from one another.
Sebastian Hassinger • 13:34
Right.
Isabella Martinez • 13:35
The people who spend all of their time thinking about, you know, the error tolerance of these quantum sensors are different from folks who are thinking about, okay, but this is going to be underwater or this is going to f be flying in a hurricane to collect meteorological data. This is going to be in space being bombarded from radiation from the sun without protection from the atmosphere. And these are all different people who often have had very different professional experiences and all of us have the same title of engineer.
Sebastian Hassinger • 14:01
Right, right. Yeah. Is that an example that sort of ruggedizing an existing sensing technology, is that how you would define a product in your kind of
Or does does it the a product in your context does that need to have a specific solution, like more specific than just ruggedizing a sensing device?
Isabella Martinez • 14:24
That is a very good question. Let me think about that for a second. I haven't thought about that angle before.
Isabella Martinez • 14:46
Now I found me like you need to do a follow-up with me in a couple of months. Okay. So the way that we are defining products tends to look a little different depending on the customer that we're working with.
Sebastian Hassinger • 14:56
Yeah. And that's kind of what I'm digging at is exactly that. Yeah. Different engagement models.
Isabella Martinez • 15:01
Exactly. And I
think within the quantum sensing space. we haven't seen the desire for a physical product grow.
There is a sense
that
quantum sensing companies or particular quantum sensors even are graduating from programs such as those that have been run s very successfully, I might add, by DARPA for many years.
Sebastian Hassinger • 15:25
Right.
Isabella Martinez • 15:26
And now that they've graduated very similar to graduating from a startup incubator, people just want to go and buy.
Sebastian Hassinger • 15:33
Right, right, right, right.
Isabella Martinez • 15:34
And the
Sebastian Hassinger • 15:36
capstone project.
Isabella Martinez • 15:37
Exactly. We're taking them through the capstone project. So it
needs to come with a lot more considerations and sometimes the original founders, original creators of these devices have thought of
Sebastian Hassinger • 15:48
well and in some cases they're just trying to create a device that's a supply chain component, right? I mean they're they're not necessarily thing I mean, a whole product takes in the context of the customers you're trying to address and they're all all the things you mentioned in your capstone project context. And that's not necessarily what somebody is making. a really exotic PNT quantum device needs to be putting their time into so it's almost like a translation f function in that case.
Isabella Martinez • 16:17
Yeah and it's several layers of translations because we Have also had a lot of really interesting conversations with our our network of collaborators around there's a translation
to other entities that are used to being supply chain components. So even the conversations that the creator of the quantum sensor, for example, is having with all of the different entities that they work with to source their own components. And then it's a translation to the systems integrator that's going to take the quantum sensor and to put it on into the space of interest. And then
I think quantum this is where quantum sensing and quantum computing begin to diverge from one another because in the ideal scenario, someone who is operating, let's say, a drone It doesn't need to necessarily know that the data that they're getting is quantum data versus the data they're getting is classical data. From the perspective of an operator, it's the same.
Sebastian Hassinger • 17:12
Right.
It's more accurate or it's more robust, or it's but it has some kind of classical attribute that presents to them as just better data.
Isabella Martinez • 17:22
Exactly. They haven't changed the way that they're interacting with the drone.
going to happen with quantum computing is at least for a time there will probably be a change in the way that people are operating are interacting with the data or with the problem. And that I think is an additional place where having an interdisciplinary team and an interdisciplinary background is very important.
Sebastian Hassinger • 17:44
Right.
Isabella Martinez • 17:44
Because there's the designing of an algorithm to make it going back to the you know, four steps that I mentioned earlier.
Sebastian Hassinger • 17:51
Right.
Isabella Martinez • 17:51
You know, there is a designing an algorithm that is sound in the sense that the quantum physics checks out.
Sebastian Hassinger • 17:56
Yes.
Isabella Martinez • 17:57
And that is sound in the sense that the algorithm runs successfully. And let's say let's make it a little harder and say that it's an optimized for the particular combat hardware and software stack that's been chosen. And
a huge industry in and of itself right now. And I think I left before our our software. Startups could finish presenting at the pitch competition, but you know
Sebastian Hassinger • 18:20
Sorry to take you away.
Isabella Martinez • 18:21
Yeah, no worries, no worries. but
But there's also and let's say that then you have to work with someone to make sure that the algorithm is being applied to a problem that has monetary value, which is the big
Sebastian Hassinger • 18:35
And in an accurate way. I mean if it's if it's if it's let's say it's in life science or bioscience or whatever, then you need expertise to almost match the physics expertise to make sure that translation is happening accurately. Exactly.
Isabella Martinez • 18:48
And that, you know, it's actually commercially viable. Is the is the extra precision or efficiency or, you know, whichever of the chosen directions one has gone with designing this algorithm, is it
Worse it?
Sebastian Hassinger • 19:03
Right.
Isabella Martinez • 19:03
How many times do you need to run it?
Sebastian Hassinger • 19:05
Right.
Isabella Martinez • 19:05
Who is running it? Who are you paying?
Sebastian Hassinger • 19:07
The economics of it. Exactly.
Isabella Martinez • 19:10
And then there's also, you know And who's gonna buy it?
Sebastian Hassinger • 19:14
Yeah.
Isabella Martinez • 19:14
And who's gonna buy it also is driven by who's actually using it?
Sebastian Hassinger • 19:18
Yeah.
Isabella Martinez • 19:18
Are these computational biologists who are already used to adopting cutting edge tools into their workflows?
Sebastian Hassinger • 19:24
Right.
Isabella Martinez • 19:24
Is this people who
have mostly spent their careers optimizing things, you know, f in databases, in spreadsheets.
Sebastian Hassinger • 19:34
Right.
Isabella Martinez • 19:35
And now we're telling them to adopt the quantum computer.
Sebastian Hassinger • 19:37
Yeah.
Isabella Martinez • 19:38
And I know that there is a there's a desire in the field to make this as simple as a command line prompt. And there's so many steps to getting from here to there. And, you know, is the u does the user want a command line prompt? Is that what the user wants?
Sebastian Hassinger • 19:52
Right, right, right. Right. And so at this stage, I mean,
That whole exploration, I mean, i many enterprises that I've seen and you know, companies, you know, whether it was I've been at IBM or AWS or partners that I've worked with. they're engaging enterprises and they're taking them down an explo exploratory path. It almost it almost doesn't matter where you start in terms of a use case 'cause the Job one is sort of let's just figure out like help you figure out what quantum is and start to develop a quantum intuition, if you will Is that part of what you're doing in your role and the team that you lead? Or are you more at the stage of like, once you understand as a customer, where you want to apply a quantum, we're gonna help with the application, the deployment.
Isabella Martinez • 20:43
We are able to do both. And I think that the amount that we are doing one over the other
has changed a lot over the years. right now we've been really focused on more education around post quantum cryptography just because of the situation that we're in.
Sebastian Hassinger • 20:59
Yes.
Isabella Martinez • 21:00
with, you know, new
policy and laws coming out about that adoption.
Sebastian Hassinger • 21:05
And the compression of the compression of the timescale.
Isabella Martinez • 21:08
And that conversation, that education, we really don't need to
Sebastian Hassinger • 21:12
It
Isabella Martinez • 21:13
spend so much time talking about quantum computing because th the cybersecurity industry understands that cyber threats are evolving.
Sebastian Hassinger • 21:20
Exactly. It fits into the threat profile, it's just a new one.
Isabella Martinez • 21:23
Exactly. And when you're talking to somehow adopting quantum sensing You all they care about is how much more precise?
Sebastian Hassinger • 21:29
Right.
Isabella Martinez • 21:30
How much is it gonna cost me? What is the operational imp like how much slower is the platform now that it's got this extra weight on top? Size, weight, power, cost. Right With quantum computing, I think we do still have an amount of education to do, but I actually I dare say that the quantum community has actually done a really good job, including certain resources like your book and your podcast.
I actually
I'm so surprised that when I tell people that I work in quantum computing, people know what I'm talking about. They might not be completely a hundred
Sebastian Hassinger • 22:03
changed
four years ago.
Isabella Martinez • 22:04
Exactly. And it like through the course of my career has been from I don't really feel like explaining to in this random networking party that I'm here with my spouse, I don't feel like explaining my job. I'm just gonna say I'm a computer engineer and hope I don't land myself in hot water to saying, oh, I am a project manager and lead partnerships for a quantum computing team and people know what I mean.
Sebastian Hassinger • 22:24
Yeah.
Isabella Martinez • 22:25
And that's very surprising. So it's more the education of how is quantum computing different from AI? Why is there space for both in the market? So that once again goes down to use case.
Sebastian Hassinger • 22:37
Yeah.
Isabella Martinez • 22:38
And it's ver it's always been very difficult to explain quantum technologies without using some analogies. But because we can now point to discrete use cases and tell someone, you know, quantum computing is not going to be optimizing your Netflix algorithm.
Sebastian Hassinger • 22:51
Right. Yeah, yeah.
Isabella Martinez • 22:52
it's instead going to be used to do really high-end
molecular simulation for pharmaceutical industry, people go, Oh, that makes sense.
And
as you said, that wasn't the case a few years ago. And the same is true with our customers, is that they're coming to us more now and saying
When am I getting this? Right. And when can it do this optimization use case that I heard it can do? Or when is it going to be able to do high speed training or what is it going to be able to actually plug in to help with the understanding of a biological component for all sorts of modeling and simulation use cases.
And
I think the AI bubble is both giving urgency to the question and also people are are like, okay, we're gonna wait and see when things are ready, because if we jump in too soon, suddenly there's gonna be this big leap in capability. And it's like, oh, all the things that we said were AI before is nothing compared to what we're saying AI now. And I think there's a little bit of that coming in quantum as well.
Sebastian Hassinger • 23:57
Interesting. Interesting. Is there
Do you see any particular
sort of trends or groupings in the types of customers, the industries or the use cases that are are coming to at this stage?
Isabella Martinez • 24:11
I don't know if I can answer that right now.
Sebastian Hassinger • 24:13
Okay. Fair. let's see. So okay. I if you're talking about
engaging in that's fine. I'll wait all the way to
Isabella Martinez • 24:25
I'm
I have not lived in the DMV for many years and I'm now allergic to something. So I apologize. Probably everything. Thank you.
Sebastian Hassinger • 24:34
I live in Austin, so I'm familiar with being allergic to everything.
Isabella Martinez • 24:37
Yeah.
All right, I'm yours again. Thank you.
Sebastian Hassinger • 24:45
okay, so when you're as you said, the question is now when. So when you engage with a customer like that, are you Are you diving right into use case definition, algorithm exploration, resource estimation? Or are you starting Are you still having to start with sort of like earlier stage discussions of like talking through what they think it's gonna be good for versus what it might actually be good for, those types of calibration conversations?
Isabella Martinez • 25:14
I promise that I'm not ducking the question. It's genuinely a case-by-case situation. I it is a little bit of the Wild West where we're getting some
you know, very clear use cases and very clear requirements and a very clear yes or no.
Sebastian Hassinger • 25:32
Right.
Isabella Martinez • 25:32
And there's other
areas where we get to explore and play really a little bit more.
Sebastian Hassinger • 25:38
Yeah. And in terms of workforce development, are you
sort of I imagine you're you've got sort of a strategy for building up your own internal resources. Are you also sort of advising and reflecting that strategy to your customers and sort of telling them
how to build those kind of interdisciplinary teams to address this space?
Isabella Martinez • 26:00
I think one of the ways that we do that is by being very clear why we're bringing the kinds of people that we bring in our team in a given situation. I, for example, at one point in time, we had some we had two folks on our team, one who was trained in chemistry and one who was trained in biology. And we sat down with a couple of folks trained in quantum physics and someone who's training who's doing theoretical quantum algorithm work because they were again building algorithms for fault tolerance that this was a few years ago, so d d definitely felt like it was far away. And it was by sitting down and talking through the actual workflow of a biologist and a chemist sitting down to do the very beginning steps of I believe it was a health use case and I believe it had to do with the generation of syn synthetic molecules in order to see if they were good candidates for a, you know, drug of interest. And they sat us down through how they actually did that. And the part of the problem that the folks with the physics background thought was gonna be better suited for a quantum computer turned out to not be the computational battle bottleneck. So even if a quantum computer were going to save time or energy on that step it didn't really matter to the computational biologist and the computational chemist who'd be actually using, you know, this theoretical tool we're we're talking through. It was in a completely different part of the problem. And we wouldn't have known that without sitting down and having several hours of deep conversation about the work, like the steps that they take as people with lab bench experience who then became computational. bi computational biologist and chemist. And that was such an eye-opening experience for me and it happened, you know, fairly er fairly early in my managerial career of being like, I need to do this purposefully. I have to purposefully open reqs where we are looking for people with more than just physics degree. We're looking for people Who have interests and have explored things reflected in internships or extracurricular activities or
you know, they've gone off and done something else for a little bit and then they've come back to physics and to quantum. Those are the people who we are looking for and those are the people who we are then bringing to our client spaces with and we're telling our clients this is why they're here. They're here because they've had this unique experience.
Sebastian Hassinger • 28:30
Right. And are those all sort of
advanced degree, PhD level degrees in physical sciences, is that sort of the main skills that you're you're needing in your team or is it is it broader than that?
Isabella Martinez • 28:44
It's much broader than that. we have folks with criminology degrees, we have folks with statistics and math and as you said, the hard sciences, also economist, psychologist. I'm a biomedical engineer. I'm not the only person who has an engineering degree. but also people who've, you know, gone off and like founded a data science company. someone who went and like founded an education startup and then came back and was a data scientist for a little bit and now is doing quantum. So just folks who really have a
a penchant for creativity.
Sebastian Hassinger • 29:17
Yeah.
Isabella Martinez • 29:18
And I think we need more of that. And I my undergraduate institution was a liberal arts college. and that sometimes surprises people to hear
Sebastian Hassinger • 29:27
I have a B in English, just for the records.
Isabella Martinez • 29:29
Yeah, no, amazing. This is exactly what I'm speaking to. No, like we need the just having had
whether formal or informal, immersion in something else. I think, particularly since quantum computing is so new and By definition, we're telling people that you are decomposing a problem and rebuilding it from scratch, that you are approaching the end use case in a different way. from classical computing. And so I'm always so thrilled there seem to be a lot of people in the quantum industry who are also musicians. Like everyone this is bringing just Skills and experiences from different fields. And I think that's one of the things that makes the industry so
fun And I think it's also something that needs to be more purposeful in the way that we recruit and also the kinds of work that the quantum economy is trying to support and the kinds of programs that we're trying to support in education. you and I talked about before we started recording about a lot of work that universities do and workforce development, but
the kinds of people who are being hired, I think shapes workforce development a lot more than any university program ever could because you can be a world class university program, but if no one's hiring your graduates Eventually it you know, you might continue to have graduates, but they're going to struggle and eventually it will move someplace else. Because that's not serving the goals of the university either if the graduates aren't being hired. And we're starting to see all of these masters in quantum algorithms development, which makes me excited.
Sebastian Hassinger • 31:08
Quantum engineering
Isabella Martinez • 31:08
too. Exactly. But it also worries me Because then there are people who've only done quantum. And the way that there were people who only did computer science and we found that they weren't bringing that creative spark to the spaces that they were in.
Sebastian Hassinger • 31:21
So if you could wave a wand and like change the education landscape, then I'm assuming you would before you sort of increase the volume of, let's say, physics PhDs or other physical science PhDs, you'd You'd want more interdisciplinary, kind of purposefully interdisciplinary programs.
Isabella Martinez • 31:39
Yes.
Sebastian Hassinger • 31:39
Yeah. Interesting.
Isabella Martinez • 31:40
And whether it's the individuals who are interdisciplinary
or
Interdisciplinary requirements.
there is some work, I forget who published it, it was a few years ago, where
It's like if you are an undergraduate with a computer science degree, but you are forced to take classes with let's say historians and artists and all of you have to do a project together, one also learns how to communicate. And I think, you know, much lower stakes when you're a sophomore in college.
But those skills are brought with you into your career. And those experien we're all made up of the sum of all of our
experiences. So that's what I would like. Either when you graduate there's some requirement for having taken classes other than than physics and computer science or you've been required to work very purposefully with someone who has a completely different interest than you do.
Sebastian Hassinger • 32:39
Right.
Isabella Martinez • 32:39
And hopefully they've both learned something. And maybe they go then write a book about quantum.
Sebastian Hassinger • 32:45
Yeah. So okay, so coming back to, you know Sensing is sort of you know on the verge of launching in various forms in terms of commercialization, deployment, production deployment.
Isabella Martinez • 32:57
Exactly.
Sebastian Hassinger • 32:58
PQC
is very much surging in terms of commercialization, production, deployment.
Isabella Martinez • 33:05
And I would say it's in a very similar space because I think something that we've seen this year is existing
organizations that have very strong cyber products and have been leading in the classical cyberspace have all
put out PQC versions of their tools. So I think that's making a very big difference.
Sebastian Hassinger • 33:24
Me too. So okay, so those two are sort of, you know, on the cusp of turning into viable, you know, real markets.
Isabella Martinez • 33:32
Right.
Sebastian Hassinger • 33:32
quantum computing is still you know, i nascent, I would say. Do you think there will be
products, customer solutions, things that you deploy for customers, with customers, with partners? i pre-fault tolerant quantum computing? Do you think NISQ is gonna be useful for anything in the near term, or are you sort of looking at you know, twenty thirty, let's say, and beyond when when f actual logical qubits are are shipping, and we're starting to see fault tolerant convenience scale.
Isabella Martinez • 34:08
I think
that we have seen evidence that really good scientific output is happening with the NIST devices. And there is I absolutely believe that there is value in science for science's sake as well. And I had actually meant to look it up before talking to you, but I think we can remove this if I'm wrong. I think it was
Someone used a NIST device to characterize a black hole. It was like wildly better than what they could have done classically.
Sebastian Hassinger • 34:41
Okay, I think which was related in some way to the black hole. I believe, if I'm not mistaken. But yeah, he did he did it on an IBM device, I think he used 103 qubits or something like that, and he did his own custom error mitigation and he did get, I mean it was a it was a better result than he would have gotten classic.
Isabella Martinez • 35:00
Right.
Sebastian Hassinger • 35:00
At least the way you described it to me. Okay. That may be what you're thinking.
Isabella Martinez • 35:04
Yeah, I think that's what I'm thinking of.
Sebastian Hassinger • 35:06
And I agree with you. I think there's there's definitely You know, I mean they are scientific instruments. We're getting real science.
Isabella Martinez • 35:13
Exactly. And I think that there is value to the scientific community in the science that's been done.
And that will continue to be of value regardless of where the industry goes. Now, I am a believer that we are indeed heading into an era of fault tolerance and we will get there and the nature of those products are going to change.
I think that
groups that are and it segments of industry that are reliant on RD, such as pharmaceutical, are going to
see those benefits in a very real way, but maybe not in a way that is
apparent
to
to someone who's not in the quantum industry, let's say. And
I think that'll be I do not feel very confident in my predictions of what that's gonna look like, but I think the early twenty thirties are gonna be very interesting for all of us.
Sebastian Hassinger • 36:08
Yeah, I agree. I agree. And I mean Going back to what you were saying about the I mean, for one thing, the fact you're attracted to the space because there aren't answers to these questions I'm very much the same. It's what it's what keeps me super engaged. And I also think that if you're collecting a group of people on your team that are interdisciplinary and also are drawn to those hard problems for which answers aren't don't preexist. There's nothing you can sort of draw on. You're much more likely to be the first people to hit on. really creative solutions to problems and applications of the technology. So I think you're taking the right approach.
Isabella Martinez • 36:48
Thank you. So that's the hope. And I think that no matter what, one of the things that was a big theme of Quantum World Congress last year was the need for sure, you know, everyone with all these different startups are competing for the same limited a pool of investment funding and the same you know, limited early small business research opportunities. But
we can't all tackle everything at once. And it does take a network. And I think approaching
not only with a mindset of creativity, but also a mindset
of
how do we work together to be creative and how do we draw one another's creativity in order to create something entirely new I'm really hoping that we don't lose that collaborative spirit as we enter into this new computation computational era that we're all helping to create.
Sebastian Hassinger • 37:41
New quantum era, you might say, is
Isabella Martinez • 37:46
I was trying to very hard to not say that.
Sebastian Hassinger • 37:50
I need the plug.
Isabella Martinez • 37:54
Oh, I forgot I'm sorry. I forgot that was the name of the podcast.
All right, right, wait, okay, okay. Let me resay that.
Sebastian Hassinger • 38:05
Okay.
Isabella Martinez • 38:06
Yeah.
I really hope that we don't lose that mindset of how all of us being creative together and working as a as a group and being collaborative is what's allowing us to enter this new quantum era.
Sebastian Hassinger • 38:18
Thank you for the shameless plug. It's a good name for a podcast.
Isabella Martinez • 38:23
It's an absolutely good name
Sebastian Hassinger • 38:25
I agree. Well, thank you very much, Isabel. This has been very interesting. I really do think it's it's it's exciting. The team you're putting together and what you're the way that you're tackling in the problem space is really exciting. So I look forward to seeing what you do. So
Isabella Martinez • 38:40
Thank you.
Sebastian Hassinger • 38:42
Thanks so much to Isabella Martinez for taking the time to sit down with me at Quantum World Congress — and for being so candid about what the work actually looks like from the inside.
The thing that's going to stay with me from this conversation is that capstone project story from her undergraduate years — the one where the engineering solution already existed, and the real problem was that nurses couldn't get time off to be trained on it. She's been chasing that intersection ever since: the gap between a working technology and a technology that actually works for the people who need it. I think that framing is one of the most useful things I've heard articulated about where quantum is right now, across all three of its major near-term vectors.
Show notes for this episode are at newquantumera.com, where you'll find links to Isabella's published work on PQC migration and quantum workforce strategy, background on Booz Allen's quantum practice, and related policy context including the NIST NCCoE's PQC migration resources. If you want to go deeper on the workforce and careers side of quantum, I'd point you to our episodes with Anastasia Marchenkova, Deeya Viradia, and Piotr Lewandowski — all linked in the show notes.
If you're not already subscribed to the newsletter at newquantumera.com, that's the best way to stay current on new episodes and the ideas we're tracking between them.
And if this episode was useful to you — if it gave you a new frame for thinking about operationalization, or workforce, or just the honest state of quantum deployment — the best thing you can do is share it with someone who's working on the same questions. Word of mouth is genuinely how this show grows, and I'm grateful for every recommendation.
Thanks for listening. I'm Sebastian Hassinger, and this has been The New Quantum Era. Theme music by OCH. See you next time.
