Quantum Tools for Government Optimization Part 2

Welcome to “HSDF THE PODCAST,” a collection of policy discussions on government technology and homeland security brought to you by the Homeland Security and Defense Forum

 In this episode, We show how quantum becomes useful now through cloud access, Python-based tools, and focused pilots in logistics, materials, sensing, and security. We balance realism on timelines with concrete wins, from port optimization to post-quantum crypto planning and AI synergies.

 Featuring:

- Prachi Vakharia, President, Washington Institute for STEM, Entrepreneurship and Research 

- Allison Schwartz, Global Government Relations & Public Affairs Leader, D-Wave 

- Dr. Reggie Brothers, Operating Partner, AE Industrial (moderator)

 This discussion took place January 22nd, 2026, at HSDF’s Technology Innovation in Government Symposium

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Transcript

Accessing Quantum Through The Cloud

SPEAKER_01 0:00

So how would so how am I interfacing with this? I mean you described a huge machine with cooling and all this stuff. How do I interface with it? Cloud.

Developer Tools And Training Paths

SPEAKER_02 0:09

Cloud, okay. It's not there's not gonna be Procre always says we're not gonna have quantum in our, you know, laptops and and phones, but you can access it. Um so yeah, I mean there's AWS Bruckhead, Google has a cloud platform, D Wave has her own Leap quantum cloud service you navigate through. One of my colleagues was actually submitting quantum uh problem sets while she was on a train in Europe and got answers back. So um, you know, it is accessible. It's not going to be on the edge. You're not gonna put a quantum computer on the edge, um, but we can actually provide solutions cloud-based.

SPEAKER_01 0:43

And what's the software? Top of the software, like what type of thing.

Real-World Apps: Scheduling To Delivery

Quantum Sensing Use Cases And Maturity

SPEAKER_02 0:46

So our ours is our own proprietary software. Yeah. Um and so uh and we work with our partners in uh developing that. But for us and our technology, we have a training program, and if you know how to code in Python, you can learn how to code for us. And we have online courses, we do in-person courses. Um, one of our a retail groceries came to us in Western Canada and they had a staff scheduling problem. And we sat hand in hand with them and built that staff scheduling application with them. They came back to us with an e-commerce delivery solution and said, we believe that we learned how to do this. Could you help tweak it? So we built it with them the first time, they built their other one. So it's really not that difficult as long as you have um folks that know how to code in Python.

SPEAKER_01 1:30

And let's go about the quantum sensing. So you're excited about the the magnetic sensing, right? So uh is that being used right now?

SPEAKER_00 1:38

Um it's being explored for testing. Sure, okay and pilots. Yeah, so quantum sensors, again, sensors do different things, pressure, someone's sensitive with pressure, and they can tell you what pressure is, temperature, gravity, like I mentioned, uh time, uh, and various other uh physical constants. Uh so that's where I think uh different technologies are. I think I would say quantum sensors are a lot more mature than quantum computers. If you do want to get started on your quantum journey today, start looking at where quantum sensors can help you. There's another quantum sensor that's fantastic for radioactive material detection, again, that has DHS applications. Uh navigation is one where we've easily found found ones. Think of again, uh, how do we all know time today? Time is because you know we sync with the atomic clocks in space. Imagine if we all had clocks that don't drift over time and that could tell us time. So even if we lose a connection at any time, we can even navigate ourselves because we don't need a clock to sync constantly, which is what we need today.

Policy, QEDC, And Reauthorization

SPEAKER_01 2:31

So if I if I if I'm somewhere where I don't have infrastructure support, right? Um how would I use this? Like where would I get this quantum sensor from and how would I use it?

SPEAKER_00 2:40

Yeah, plenty of companies in the US today here, uh as well as academic groups that are looking at quantum sensing as well. Um there are certainly hubs. There's a group called Quantum Economic Development Consortium, QEDC. It's run and it's uh supported through a White House initiative. And I think um I'm not sure exactly the mechanism, but it's a national consortium, and certainly the members are someone.

SPEAKER_02 3:07

Right, and was reauthorized recently as well. No, it hasn't been reauthorized. It expired in September of 2023, and Congress just worked so slowly. But the reauthorization bill has been introduced in the Senate, and the House is having a hearing today, and hopefully, hopefully we can get it done this Congress. Okay.

SPEAKER_01 3:33

Pratt, another area of interest of yours is education.

SPEAKER_00 3:36

Yes.

SPEAKER_01 3:36

Right. So tell me how are you thinking about education in this area?

Training Mindsets And Post-Quantum Crypto

SPEAKER_00 3:39

Yeah. Two things for education. It's not only bringing uh people into quantum or pivoting people into quantum, it's also making decisions, uh, decision makers aware of how quantum can impact them. And I think conversations like this is where are really important, where you know what does quantum mean for all of us? And for me, it meant like watching a bunch of videos, understanding what quantum meant, yeah, come from the transportation background, and then putting a workshop together, really inviting the quantum community and the ecosystem and agency I work in and saying, like, let's talk and let's figure out what is the application. But I think you know, quantum education is something we'll all have to do. It's a bit like cloud education. I think we all learned how to deploy things on the cloud, how to move. I was hearing lift and uh ship lift and shift earlier. Uh, we had to all learn this language, and I think it's just part of modernization. It's also part of national security. How do I we keep ourselves abreast of quantum and understanding I think basic uh uh technology and terminology, like what does quantum key distribution mean? You know, how can quantum computers be hacked? Well, how can quantum hack classical computers? All of that I think is going to become a question very soon. And the better and faster prepared we are, I think the better we will be as a country as well.

SPEAKER_01 4:43

So, one of the things you just brought up um hacking and all that. So, Alison, do you want to talk about some of the quantum encryption and some of the challenges there?

Quantum x AI: Synergies And Projects

SPEAKER_02 4:49

Sure. Let me just go back a little bit on the training side because I I see training as twofold, and then I'll get to the encryption. Training is twofold. One is knowing what quantum is good for, and that is you can spend so much time and money and realize that you were actually running down the wrong path. So understanding what quantum is good for is important, then understanding how to develop applications and develop the codes that you need for it from a computer, which sensing device. So think of training and upskilling as two different types of mindsets. One is making sure you understand what is ML best for or AI or HPC or quantum from the compute perspective, and then how do I actually get my workforce actually able to develop in this? So, from a workforce development, think of it from those two mindsets. Um, from the encryption perspective, um, I will say I agree with Proci in the timeline. So the current RSA encryption is not in any uh danger of being broken by a quantum computer anytime in the near term. Um, there are work that NIST is doing on post-quantum cryptography. Most of that work is actually classical solutions in order to break a quantum computer once it theoretically can break it. Um, Google did a study years ago. Um, we mentioned different types of qubits, and it would take, I think it was something like 20 million superconducting qubits to all work together to break a current RSA. I think we have a thousand qubits in the gate system that don't all work together. So we are well away from the 20 million, and in photonics and ion traps, you need a billions of qubits, and you're at like 64, 67, a couple hundred. So again, we have many, many more years, but understanding your data, making sure it is behind post-quantum cryptography, starting to morph it. Um, it takes forever for those in the government um in the audience. Um, it takes a long time for governments to move. And so you do have to kind of start that work. And I know that there's already work there today. Um, and then for those supporting the government uh in the audience, you're going to be mandated to do it. So um navigating that post-quantum cryptography is going to be important. And sometimes it could be a a quantum solution like quantum key distribution, other times it will just be a classical solution like the post-quantum cryptography that uh NIST is working on.

SPEAKER_01 7:14

So um there's a lot of things we still need to talk about here. Um one is well actually the the session after this is on AI, right? So how does how does quantum interact with AI? Are there some advantages for quantum for AI? There's synergies there?

Benchmarks, Materials, And Grid Forecasting

SPEAKER_02 7:29

Yeah, so I was really excited with the Genesis mission announcement because it really kind of for the first time theoretically broke down the silos in government. Explain with the Genesis. Oh yeah, so Genesis Mission, um it was part of an uh an announcement that um the White House put out. It includes kind of all emerging technologies from fusion to quantum to AI and others, and it actually reorganized some areas like you now have a quantum and AI office within Department of Energy. Um, and so everybody is kind of running at the same path. And usually the quantum programs were siloed off from AI, and that is actually now how it's going to be implemented. Um, I mentioned uh a little bit earlier in the panel that we have done some optimization of some modeling of uh LLMs. This was in a drug discovery area, and we were able to uh actually cut the time and the amount of energy consumption used and get a better model for this drug discovery. It was a pharmaceutical division of a Japanese company that was doing the drug discovery. We're also doing it for a Gentic AI with a Canadian national lab as well as with uh ULUF Supercomputing Center in Germany. So there is going to be a synergy with this. AI has some amazing capabilities. Quantum, especially from the compute perspective, is just that complex compute portion of it. So again, you're gonna need both of these. You're gonna have to have your staff trained up on them and understand as you're building out a staff that knows how to code on AI and ML, get them learning how to code on quantum now. There's, you know, so many people who are doing it. And unfortunately, if you don't start learning now, you're kind of almost already behind.

Port Optimization And Logistics Wins

SPEAKER_00 9:10

Yeah, I would very proud to say Advisor. We have one of the largest quantum machine learning experiments deployed on IBM computers today. And that is with a partner called Eon, which is one of the largest uh grid companies and grid operators in Europe. And there we're looking at a time series forecasting uh using real-world energy grid data coming um coming into the systems and then doing the forecasting with it. Um, something else we are doing is also a lot of benchmarking work. So, our quantum algorithms, how do they compare to the classical ones? And if you ran the same algorithms again on HPCs, high performance computes, what are the results? So, again, I think Alison mentioned this earlier. Not every problem is a quantum problem. Some things can be solved using classical computers, and that's what they're really, really fantastic for. Use them. Same with HPCs, use them. Uh, one example I would like to give is where we're seeing the really value of quantum and earlier examples is anything with material science. And I'm curious here, any of you maybe looking at materials. One of the partners for visor is uh the US Naval Nuclear Lab. And if you think of the US Naval Nuclear Lab, they build the nuclear part of our nuclear submarines. And you know, other things they're looking at, you know, within the submarine and the nuclear part itself are things like coatings and alloys, you know, outside the submarine. That's one of the hardest problems, right? How do you prevent rust? How do you prevent barnacles from sticking to your uh submarines? And this is again where material science is really, really important. Or think of corrosion as well, corrosion modeling. Actually, that's a really good quantum problem to have uh for quantum computers to solve. So start thinking of what are the kind of problems that quantum is really good at, and I think that's a great way to get started on that journey.

Which Problems Fit Which Quantum Model

SPEAKER_02 10:42

Yeah, and and I think on as you're looking at kind of where Proci was talking about from those problems, that's part of those are good for quantum annealers, part of those are needed for gate model systems, and so understanding again the capabilities of both. So, you know, D-Wave did a quantum supremacy with Oak Ridge, where we solved a quantum magnetic material simulation problem. Um, we solved it in 20 minutes using one dollar's worth of energy. It would at Oak Ridge would have been up to a million years to solve the same problem with the entire annual cost of energy. So, from an AI perspective, it's that energy modeling that's happening with it. But the quantum chemistry side, so we've done the material simulation, now we have to develop it and use a quantum chemistry. You're gonna need a gate model system for that. But what we did on that supremacy side, we actually realized we can do quantum proof of work in um hashing for blockchain. And that's a new research area. So, like as you're looking at it, these new applications are coming out. Again, I've been with the company five years. I was never even thinking my history in FinTech with blockchain was ever gonna port into quantum, but here it is. So it is an emerging field, and so there's so much more happening.

SPEAKER_00 11:54

Another example I think the audience would like is also some of the work you did with ports and optimizing port operations.

Measurable Impact: Cost, Speed, Emissions

SPEAKER_02 11:59

Yeah, yeah. We worked with um a customer and they optimized uh cargo pier at the port of Los Angeles, increasing truck turnaround time by 12%, increasing cargo per crane by 60%. And so again, what this the they built it actually as a digital twin and then looked at um every single thing that they could possibly do, optimize it, change the entire method of the port, and then in production did a pilot where because they used to do first in, first out, a truck showed up, you found the cargo, you loaded it, you left. Um, now because trucks come early, they come late, things were happening, um, they actually could pull out the trucks that showed up based on where the cargo was. So being able to optimize that port, optimize that port logistics, but think of that, that could be done at a submarine, that could be done at a military base, that could be done, you know, for optimization of manufacture of maintaining DHS fleets. I mean, there's a variety of these dis different applications that could be used.

SPEAKER_01 13:02

Let me see if you guys agree with this, and please push back if you don't. I think when I think about what kind of problems are best solved with quantum systems, um, it's it's combinatoric kind of problems, right? And this is because of superposition property that you brought up earlier, right? And annealing is a particular type of optimization problem. And so if you can put your problem statement in an anneal in the annealing equation, that optimization equation, you can then use that method. If you need another type of optimization, maybe you need a gate model, right? Do you agree with do you agree with that?

What’s Next: From Theory To Products

Closing: Prototyping And Getting Started

SPEAKER_02 13:30

Or not not not fully. Um so the research has shown that optimization is probably best, always gonna be best on an annealer, but quantum chemistry differential equations, yeah, you're gonna need the gate model system. Okay, okay. It's a linear linear algebraic question, things where factoring sits and all that, either one, and we're not sure because we can't benchmark between them. Okay. Um, so that's kind of it. For me, the question to ask as the non-technical audience is um you look at it and say, is your solution not good enough now? Does it come too slow? Can I not get the right solution? Can I figure out a better way to do this? And sometimes, you know, we can shave off 5%, 10%, but that's millions of dollars and multimillions of dollars. You know, in Tokyo, we did an optimization of waste collection. We were able to collect waste, reduce the amount of trucks on the road, we were uh able to reduce the CO2 admissions by 60% by not having as much trucks. If we looked at one area of, let's say, CBP and were able to do the same operations but do it more efficiently using less trucks, using less, how much money would that save the American taxpayer? How much money would that be able to be redeployed for something else that's critically mission sensitive? So it's sometimes it's about is there a problem that's outside the scope? Sometimes it's I need a better solution faster. Sometimes it's can I just find a a a way to save a little bit of money and redeploy it to something else?

SPEAKER_01 14:57

Excellent, excellent. Thank you guys. I think we're gonna spit uh go to questions from the audience. Any questions? Any questions?

SPEAKER_00 15:07

You got a free quantum computer.

SPEAKER_01 15:11

I'll tell you, there's no no questions right now. Um I I do have another question. Something we talked about yesterday, um research. So, where do you think that the next phase of quantum is going? Where do we need to look?

SPEAKER_00 15:22

Pratt, do you want to take that? Yeah, I think simply put is implementation and getting better and better. So, quantum for a long time was very theoretical. We needed physicists to help understand it and then help. It was very much lab-based. Now we are seeing we are able to have uh larger qubit devices and we have more and more engineers come into the space. So I think this is where we are doing, I mean, companies like Sky Quantum are building extensively in terms of the infrastructure itself. They have facilities, I think, at least in four locations. So I think quantum is nearer and nearer than you think. It's going from again science to engineering and hopefully soon to productization.

SPEAKER_02 15:58

For me, I think the the future is identification of those applications. So you had um uh under Secretary Emile Michael identify six critical technology areas contested logistics, AI, quantum. But quantum can help with a lot of those things. Like AI, we've talked about, and like contested logistics, it's a big optimization problem. Um you had Secretary Driscoll of the Army talking about quantum for transportation. You had the CTO of the Navy talking about quantum for logistics. I think you know a lot of those are going to be hybrid. So I do think the near-term future is going to be this huge opening up of quantum applications. The inspector general actually at Department of War just released a report saying that the department failed to follow uh fiscal year 21 NDAA that said find the problems in one to three years that you can solve with quantum and provide an annual report. They basically said in the beginning we can't do anything and then never look to those applications. So I think the near-term is really gonna be that robust idea of what can the army do, what can the Navy do, what can the Coast Guard do, what are the different components at DHS and those applications? That's kind of the near-term excitement while the companies are figuring out how to scale.

SPEAKER_01 17:12

So so there's a chicken and egg problem, isn't there? Because if people don't know this that well, right, they're not gonna come to you with kind of problems. So what how how do we fix that? How do we help that?

SPEAKER_02 17:20

Uh I think through Kratos, I think through prototyping. Uh D-Wave is the only quantum system that I know of that's on uh the trade wins marketplace platform. So we can build prototypes and navigate through that. And um, you know, frankly, you have allies that are leaning in on applications where the US was focusing more on the hardware. Um, and so for me, that's kind of where it is. But it it really is just rolling up your sleeves. And the minute you have one success, everybody else is gonna say, Oh, wait, I didn't realize we could do this. We should come over here. And because it's cloud-based, it can actually be integrated into one pane of glass. Got it.

SPEAKER_00 17:56

Yeah, maybe I'll just add in terms of applications. Reach out to me, reach out to Piser. I think organizations like ours, um, the couple of other research organizations that help and build applications. Again, you want to understand how to even get started with quantum. And initially, it is that prototyping, it is that piloting, maybe having six-month, twelve-month research projects in different areas, understanding, well, where does quantum really give me an edge? Where does it not give me an edge? Can I try one of these hard problems I have on a quantum computer today? Test it and see, well, is this something I want to spend and put in more RD money into? Uh, but the first step is always asking, well, how do I even get started? Um, and you know, same with Vanguard, we're actually starting with three research domains for them. And then as they understand the value of quantum, then we we expand from that side.

SPEAKER_01 18:43

Great. That's the last word. I want to thank our great panelists.