Quantum AI: The Future of Cybersecurity

Patrick Spencer:

Hey everyone, welcome back to another KiteCast episode. I’m here with my cohost, Tim Freestone. Tim, how are you doing?

Tim Freestone:

Good, how you doing Patrick?

Patrick Spencer:

I’m doing well. This is going to be a really interesting conversation. Needless to say, this is the first podcast we’ve done with someone in Columbia. Uh, John Biniere, uh, he’s an entrepreneur and technologist and subject matter expert in artificial intelligence, quantum computing, cybersecurity, business analytics, among other things. He currently is the CEO at AnyQ, an AI platform that empowers companies to use data to make… Better Decisions, he’s also the CEO because he doesn’t have enough time being the CEO at one company at Spin Quantum Tech. It’s an adaptive cybersecurity collection of quantum algorithms. And we’re gonna, I’m gonna have him talk a bit about that in a moment, I wanna hear more about what this collection looked like. Jean is based in the digital capital area of Bogota, Columbia. He has a long list of successful entrepreneurial ventures. and specializes in helping business leaders and owners to grow their company. Sean, thanks for joining us today. We’re looking forward to this conversation.

Jean Bernier:

Hi, Patrick, Tim, happy to be here and honor and well, thanks for the invitation.

Patrick Spencer:

Yeah, we appreciate your time. Um, so talk a bit about the two companies that you run. How do you find time to be the CEO of two companies? Uh, they both do different things. You know, what, what gave rise to them? I think you’ve been running both of these companies for one, uh, about seven years and the other one about five, four or five years, I believe.

Jean Bernier:

Yes, two companies, but the real fact is that it’s the same business model. So, NEQ is the father of the company and the SPIN Quantum Tech is the research unit that integrates AEI and quantum computing. So, it’s one company from my perspective, one business model, two legal entities, but we drive with the same team, with the same effort and it helps us to grow faster. But two companies is more for the focus because you could attract more attention and have the focus on AI in the case of AnyQ and on quantum with the case of SQT.

Patrick Spencer:

You’re specialized in two of the hottest topics in the marketplace today, I think. How you were,

Tim Freestone:

Yeah, that’s it.

Patrick Spencer:

you know, we’ve been talking about AI for six, seven months, at least. And those of us who were interested in the subject, we’ve been watching it over the years, but it really, you know, hit a hundred miles an hour back in late November, I guess, with the chat GBT announcement, you know, but you’ve been doing it for much longer, talk a bit about, you know, what you guys do from an AI standpoint and how you’ve seen it. evolve over the years and then particularly what you’ve seen and what you think about what we’ve all witnessed over the past six or seven months.

Jean Bernier:

Yes, what is happening with generative AI, chat GPT with Google, Spam E, the announcement from AWS, Amazon that shows last this month about generative AI too. All that is exciting. I have been working with AI 22 years as professional in my life, so I have the opportunity to how AI is evolving in the process. And we identify new processing power, new algorithms, new technologies, in the new programming language. So, well, I think we are living an interesting time with AI and something more amazing comes when AI has the opportunity to run with quantum. And I think the next 10 years, not only for AI, but specific in that case, the joint with Quantum will be something amazing because for first time, we will have the opportunity to execute models that don’t rely in zero or one. I know that Quantum runs on zero or one, but the superposition power, the parallel limbs, gives us an amazing and huge opportunities. So. I think if you feel that chat GPT today or the generative AI is amazing or exciting, the next 10 years for people that will create solutions with AI and these technologies, well we don’t have limits. It would be amazing things.

Tim Freestone:

You know, it’s interesting. I’m so glad you’re on the podcast because I’ve been thinking about the quantum element of this since, you know, November, basically, because we all know, and hopefully I’ve got this right. If not, I’ve been saying it wrong for a while now, but the AI models are essentially limited by the processing power, right. And the, the chips and the amount of chips and the amount of servers. And it just, the more of, of these. these chips that are built specifically for this processing power, the more of those you have, the more effective the model will work essentially. And the whole point of quantum computing is the processing, right? Is making that not just significantly faster, but in orders of magnitudes, it’s almost uncomprehensible because of that superposition you’re talking about where it’s… processing the zero and the one at the same time. And it just seems like when these two concepts come together, that will be, for me, at least I think that’s where the singularity happens, where the quantum processing and AI come together. The question I have is, you said 10 years. It seems like the industry is really struggling with quantum computing, getting it to be stable. I mean, they’re basically processing on atoms. Um, what makes you think we’re in a, we’re in the decade, um, frame, uh, or timeline of quantum computing actually being realized.

Jean Bernier:

Before answer that, Tim, something that limits AI today is not only the processing power based on the classic chips that we have on transistor.

Tim Freestone:

Mm-hmm.

Jean Bernier:

There are other two limitations. The first is the models behind because we are using maths models. AI on the background is only maths. It could be statistical calculus or probability in most simple way. When you go to deep learning, you are working with probability most of the time. When you work with supervised learning or unsupervised learning, you are working with a statistical or calculus. So one limitation is because we have many data today, but try to find the right pattern into that data with a math model with the processing power that we have today. are the limitation that we have. So with quantum computing, we could explore new maths models, new ways to work, more variables. Let’s say something that would be a new development process is the multivariable calculus, let’s say in that way. We will have the opportunities to solve real multivariables challenge. better than today we have that opportunity. And that means redesign or re-explore new math language. That will be one of the things. The second is quantum give us the opportunity to manage more data and find better patterns into that data. Because today AI, you try to find the best path for a solution, but… That best part doesn’t mean that real is the best in all possible solutions that you could work. You try to create the best path every time or every step of the process. If we review in deep learning, using the back propagation, for example, you try to do one step, review if it’s the right solution and try to say not to the older parts and… try to find another path. With quantum and with the parallel limbs, it means that the superposition gives you the opportunity to validate full paths, all the paths that we have.

Tim Freestone:

all at

Jean Bernier:

And

Tim Freestone:

once.

Jean Bernier:

that means you could find better patterns into the process. And that is the purpose of AI. AI in the most simple way is about search algorithms. you are

Tim Freestone:

Mm-hmm.

Jean Bernier:

always looking for a pattern. It is a search process. That

Patrick Spencer:

Well,

Jean Bernier:

creates

Patrick Spencer:

AI discovered

Jean Bernier:

with.

Patrick Spencer:

these new math models, John, I’m curious, will AI discover these new math models on its own or will it require supervised learning from mathematical geniuses like yourself or others to feed those into the system? You know, how do those get uncovered?

Jean Bernier:

I think it would be a merge of two. If you just think in ChatGPT, you try to use ChatGPT and if you make the right questions, you have access to, let’s say, unlimited data and database and knowledge and experience. So if you make the right questions, you find answers that really amaze you and you didn’t know before. and you could create solutions making the right questions. Questions is the first step of the scientific process. That means create a new match language will be a merge between use all the knowledge and power of AI, creating patterns, identifying patterns, with the mentoring or guidance of the right questions from- mathematicians. Mathematicians are scientists from physicists, from biologists and many other people that have the right mindset and might structure to create questions. That would be the developing of the new language. And Tim asked about the next 10 years of quantum. I think quantum is something that that breaks the Moore law of the chips or the traditional computing that say that every 18 months the processing power is the double. Because one qubit more in quantum means an exponential growth of the processing power.

Tim Freestone:

Mm-hmm.

Jean Bernier:

So if we review the history and I think the… If you are working or trying to learn quantum or start in the quantum world, you need to start understanding IBM Q, the IBM technology. It’s the first start. You need to review the Qiskit. And if we check 2017, IBM released a five qubits machine. 2018, they have a 20 qubits machine. It means not only 15 qubits more. It means exponential 15x more processing power.

Tim Freestone:

Thank you.

Jean Bernier:

2019 they released 53 qubits machine and that means the singularity processing power of the full humankind for 2019. So that machine could solve big, yes, biggest problems that we could solve with the traditional computing for this time. And in the last year, yes, 2022, they released 552 or 53 qubits. I don’t remember the right amount. That is something amazing in the process. And they say on May, 2023 this year that for the next 10 years, for 2033, they will have 100,000 qubits machine. So that

Tim Freestone:

And

Jean Bernier:

will

Tim Freestone:

it’s,

Jean Bernier:

be.

Tim Freestone:

it’s

Patrick Spencer:

unfathomable.

Tim Freestone:

all machine. It’s, it’s all machine in a big warehouse. Is that correct? That they are using.

Jean Bernier:

Yes, so we could solve problems that today is only a dream for the minds. We could find with that machine you could solve matrix problems for let’s say for 10 million, multiplied by 10 million complexity of variables. The next 10 years and not only the next 10 years, I think quantum machines will have development and advantage for two centuries more because it’s not only at qubits. We today are using only two properties or features of the quantum world, the superposition and the quantum entanglement. but the quantum world has many other attributes that we could use for identifying information, to process information, to share information. So in the next two centuries, quantum computing will give us more knowledge and advances of the universe, of the quantum world, and using them to solve problems, not only computing. So it will be a totally… totally different world that we could create a mindset using the quantum.

Tim Freestone:

Is

Patrick Spencer:

And

Tim Freestone:

IBM

Patrick Spencer:

are we at the-

Tim Freestone:

applying this to AI? Are they applying this machine that they’ve built to large language models and trying to create the super AI, the AGI, or is it more about just creating the stable machine right now? Not really using it for anything.

Jean Bernier:

I’m not sure what is happening with AI about IBM. Most of the time in the history, they are the leaders and create better or an amazing technologies. But for some reason, they are not the leaders on the market and others take the advantage and the opportunity. So they are building amazing quantum technologies. The IBM Q, I think is very good technology, but today there are other hardware developers on quantum that are making very nice solutions. For example, the hybrid simulation from the Wave or Toshiba, they are making for optimization, amazing technologies too. I think in China, SpinQ, a company with a name similar to my company, Spin Quantum Tech, so they have… The most interesting fact from them is they have, let’s say portable quantum devices because it’s something that you could drag and drop to your car and move to other part and plug and use and use in normal ecosystem and environment. So that

Tim Freestone:

Hmm.

Jean Bernier:

is interesting. So IBM is making amazing things. They have… an amazing AI on Watson and the new redesign of the full IBM Cloud. But I think talking about AI, there are other players more advanced than IBM. I think the move of Microsoft to make investment in OpenAI, they make a very good strategic move and make them. to have a good position on the market. Before has OpenAI, Microsoft Azure has a good offer of AI services and cognitive services on the Cloud. They were well-positioned and now, maybe they could be the leader of the process. Interesting things happening on AI and I think IBM is not on the leaders today. From my perspective, I could be wrong.

Patrick Spencer:

Is there a democratization of quantum computing happening now, or has it already happened? As I remember when I was at Sun Microsystems back in the day, getting old, only the big enterprises were capable of acquiring quantum computing capabilities. Whereas today with AI and some of the changes that are happening that you just described in quantum, it seems that it’s not. The technology is now available to businesses of virtually every size. And I noticed that you presented at an event, Unleash Your Company’s True Potential. I have a suspicion that probably related to AI and or quantum computing in some manner. What’s your view on that front?

Jean Bernier:

uh, some micro systems, it makes me feel nostalgic.

Patrick Spencer:

Hahaha

Jean Bernier:

Uh, you make me trouble in time almost 20 years.

Patrick Spencer:

Polaris and E10Ks and E1012s and everything else, right? So.

Jean Bernier:

Yes, I used before Solaris and the processors have operating systems from some microsystems. Amazing technology. Something different and that makes quantum the right time to burn is that quantum burns in a world of cloud and the world today and companies and people, they or we are we are use cloud today. For many people, it’s normal to pay for your email on a cloud service or use any solution and collaborative solution, have your CRM, your ERP, and many services on cloud. Indeed, we are using today a platform based on cloud for us this conversation. Before the cloud, companies need to have on-premise and license and the full data centers. And that… makes technology access expensive, but quantum computing, they born on cloud. And today you could go to Azure, AWS, Google Cloud, IBM Cloud, and other clouds, create your subscription, add your credit card, and you could have access paying a few dollars access to a quantum machine

Tim Freestone:

Hmph.

Jean Bernier:

and start creating quantum solutions, algorithms, learning the process. most of them has a free offer for start with quantum machine. So with quantum computing, so quantum born in a democratic size ecosystem on the cloud. And you don’t need to have the quantum machine in your desk at your site to create algorithms here. So that empowers a totally new world of quantum algorithms and quantum solutions and software. And today there are thousands, and maybe it could be millions of people creating or at least learning algorithms and working with quantum software.

Tim Freestone:

So how does it play into what you’re doing with your businesses and how are you using quantum computing and AI and how are you trying to put a ding in the universe?

Jean Bernier:

We are using Quantum in three specific applications. The first is data encryption because Quantum gives many benefits but also creates a new challenge because Quantum could solve the hard match on the actual algorithms that we have for encryption and could break the encryption simple. So we are creating new data encryptions, that don’t rely on the maths complexity. And we use a method that we create with my team that calls entropic encryption. And we use entropy and chaos to protect the information, to encrypt the information. Something interesting is that we are running today the algorithms and you need at least 17 times bigger quantum volume machine. to break our algorithms. So it is really secure. And it’s different from the quantum key distribution approach. The second application that we use today is optimize the operation of the companies. Let’s think in that scenario. A manufacturing company, they need to work with inventory, staff planning, logistics for the last day delivery, last mile delivery. So that planning process most of the time they use spreadsheets and try to make the best effort. The most advanced manufacturing companies, they could pay for amazing softwares with algorithms and processing power to solve that challenge. And it takes a huge investment, three million, five million dollars more of the times. But the 90% of the manufacturing or retail companies are small and medium. They don’t could pay for that kind of technologies and it’s pretty cheap. That’s a give you the opportunity to be, or to have an optimum execution. With quantum, we have a solution that enables that companies to understand where is the best time to move your materials for your inventory processes. What is the best time to purchase for your suppliers? How many, how is the… best amount to have in your warehouse, how your staff planning, how you could not overcharge people or pay extra cost in the process, how you need to adapt your logistic on the last mile delivery. And today, this solution is saving thousands of dollars or hundreds of dollars per day to that manufacturing company that they don’t think that is possible. And at the end of the process, If you as company could save per year $100,000 and your revenue is only 2 million, that is a big impact. 5% of more profit for your company just to be optimized in the process. And the third application that we use with Quantum is Accelerate the AI training for the algorithms. One thing that takes many times in AI today is, the time for training your algorithms when you have many information. And that is a real nightmare most of the time. So we use a quantum to speed up that training process because you could use GPUs, but GPUs is expensive, they need a lot of time. And you also need to restrict to the best scenario that you could. train or adapt in the train process. With quantum, we could accelerate and test all the possibilities of the full path.

Patrick Spencer:

Thank you.

Tim Freestone:

That’s fantastic. You mentioned encryption and you were, the first thing you were talking about was building your own encryption algorithm. It was my understanding that quantum computing, maybe it’s just in its current state and maybe eventually this doesn’t ring true, but it would take millions of years to break AES-256 encryption with, even with quantum computing. I must be wrong there. At some point you’re anticipating that There’s enough qubits with error correcting capabilities to be able to break AES 256 encryption.

Jean Bernier:

I think it is a different approach. If you try to make a brute force attack for an encryption algorithm, the mindset that you use with classic computing is try the first combination, try the second

Tim Freestone:

Yeah,

Jean Bernier:

combination,

Tim Freestone:

let’s go

Jean Bernier:

and

Tim Freestone:

to

Jean Bernier:

try

Tim Freestone:

a coffee.

Jean Bernier:

many options as you can. But when you use quantum, you don’t try a sequential approach. a mindset of how I can identify the right answers if I have all the answers at the first time. And the search pattern algorithm or the search mindset for the algorithm changed in the full process. We made experiments with quantum using the Gruber algorithm, that is an algorithm for search on quantum that you use an oracle to find a solution. simple encryption algorithms. Something interesting that we found is that quantum could find different encryption keys to break the algorithm, not only one as you use when you encrypt the data. That is something interesting because

Patrick Spencer:

So there’s the additional key that can be used beyond the one you create when you encrypt the data initially.

Jean Bernier:

Yes, yes.

Patrick Spencer:

interesting.

Jean Bernier:

When you encrypt a data, you have the public and the private key and you think or the mindset is this is the only encryption key that I could use to encrypt or decrypt the message. But Quantum, finding different patterns, they found, let’s say, strange characters into the full page of characters that the computer could have. is not about the same encoding is something interesting. And you could find at least three or four new ways to decrypt the information. And that is interesting because if you make the test only using one character as the encryption key, you could find at least four different options to decrypt the algorithm using quantum. But if you use two characters for encryption key, it could grow to 16 or 20 alternatives. Totally strange that characters that Quantum found it doesn’t have any representation in the UTF or ASCII or any other encoding pages that we use, but breaks the algorithm, breaks the encryption. So it’s not about, for example, if you use RSA that is based on huge prime numbers, we think today, that you could break only with the two prime numbers. But what if Quantum could find a different pattern and breaks the algorithms? This is not about if you have the right prime numbers that the entity used to encrypt the information. This is about how I could access the information no matter if I have the right or not prime numbers. So when you save patterns, imagine in that way. When you encrypt data, you are trying to hide information in a universe of chaos. And you design with the encryption key only one path to access this information. But in the universe, in a multivariable dimension universe, you could find another way to go to the same point.

Tim Freestone:

So

Jean Bernier:

The thing

Tim Freestone:

it’s basically,

Jean Bernier:

is…

Tim Freestone:

it’s inventing characters and inventing patterns that are uninvented, I guess.

Jean Bernier:

Yes, it could be. Not invented. Discovering patterns

Tim Freestone:

Discovering,

Jean Bernier:

that we

Tim Freestone:

I guess?

Jean Bernier:

saw before.

Patrick Spencer:

You haven’t discovered yet.

Jean Bernier:

What…

Patrick Spencer:

You’re discovering

Jean Bernier:

Yes.

Tim Freestone:

That’s blowing

Patrick Spencer:

new avenues.

Tim Freestone:

my mind. It’s… Yeah.

Jean Bernier:

I like to use the example of the gravity and Newton. Before Newton understood the gravity and put the gravity into a formula, the things are still falling down into the world. that doesn’t just fly, everything’s falling down before we have the formula. So the patterns and the new paths to access the information are really there, they are in the information that we have. But the mindset that creates the things that we understand today is only between zeros and one. Quantum computing give us the superposition, that means 0…. anything and quantum finds new ways in that pattern

Patrick Spencer:

So

Jean Bernier:

or

Patrick Spencer:

the

Jean Bernier:

in that universe.

Patrick Spencer:

rogue nation states figured this out, John. Are they, you know, China, Russia, have they figured this out and they know how to decrypt what’s encrypted in AES-256 today using quantum?

Jean Bernier:

Well, I don’t know much about how governments are using AI, quantum, sorry, but I think many governments today, they are using quantum computing and improving and having the technology. This year or late last year, the NIST made a challenge about create quantum algorithms and it was something controversial, let’s say, because one of the algorithms… It was broken with a processing power of 20 years before. So, well, I think we have many things to discover here. Quantum, and that is important to understand, is not working today. It’s a baby, a baby with, let’s say, only one month of life.

Tim Freestone:

Yeah.

Jean Bernier:

So we have many things to see in quantum today. Machines with… 500 qubits or 2000 qubits in one or two years. We also have many challenges to solve in quantum. We need to extend the coherence times for the quantum machines to execute long time algorithms. We need to still working on error corrections because quantum today is still probabilistic programming process. So well, many things to discover. And for people that joins here in quantum. Enjoy the process.

Tim Freestone:

Yeah, just amazing because we’re living in a time where the advancements in technology is at such a pace and there’s two big confluences, there’s AI and quantum all happening right now that on some side you’re, I mean, anticipation of what this could bring to humanity. But on the other side, it’s incredibly terrifying over the next two or three years, what’s going to happen. because we just don’t know. Are you at all a doomsdayer on this side of it, or do you only see the upside?

Jean Bernier:

I think more than the vision of what is happening, something that I always say is ethics makes the difference here.

Tim Freestone:

Mm-hmm.

Jean Bernier:

And when you work with the technology, as the developer, the company, the CEO, and you lead a company that works on AI, quantum, and other disruptive technologies, ethics is the real difference about it. You could use a hammer to build a house or to kill a person. The problem is not the hammer is the purpose that you use the tool with quantum and AI is the same. You could use AI and quantum as IBM to find new medicines to find. But the COVID solution, sorry, the word in English. I forgot the word.

Tim Freestone:

vaccine.

Jean Bernier:

Yes,

Patrick Spencer:

Good

Jean Bernier:

yes,

Patrick Spencer:

luck.

Jean Bernier:

thanks. You could use AI and Quantum as other companies to create better food, better vegetables to improve the ecosystem, reduce the CO2, but also you could use AI and Quantum to… stole for the banks or to break the security or to make bad things. The difference here is the ethics and I prefer to be for the right side of the ethics. So my companies, my full team, our clients, all of them has the same approach in ethics. We are in the same line. We only use those technologies to make or create things that really create a positive impact for people. for health, for society, for the world.

Patrick Spencer:

Now you talked about this new encryption capability you created with quantum and AI, the confluence of both of them. I think you called it entropic encryption. What’s that like? What makes it different than the current encryption capabilities on the market?

Jean Bernier:

The current encryption is based on the complexity of the math model. So for example, the RSA, they use prime numbers with 20, I think it’s 23 million characters on the prime number. That is a huge number. You need a lot of memory on computing to create or at least a storage that number. But for quantum, It is simple to solve. Doesn’t matter the complexity on the maths behind. So our approach is not based on maths. We are using the chaos and entropic that are on the world, on the universe, in anything. And we are not encrypting the information. We are hiding the information into the chaos. So you have the information in your hands in a… let’s say word media, something that you could move and has access to it, but if you don’t have the specific chaos and or entropic pattern, you could not find the real message on this.

Tim Freestone:

Mm.

Patrick Spencer:

soon. So you see AI and quantum being used in the encryption arena when it comes to cybersecurity. Do you see other ways in which the confluence of the two technologies will be used to help organizations protect themselves from malicious actors?

Jean Bernier:

Yes, I think many cybersecurity solutions like the SOCs, the SOC solutions or the real-time monitors for companies, for your computer, I think the antivirus needs to evolve to something more powerful. These solutions are the only way and you need to have your AEI, let’s say, guard. based on or supported by quantum, identifying patterns in real time because hackers today, the attacker, they are using AI. They are finding new ways to create ransomware to extract information to attack. So AI also, and powered with quantum, is the new way to fight against the hackers and protect information. I know that today there are companies using quantum. Indeed, we are working with a company in the USA. They call Anti Fraude. They are a USA company created by Colombians like me. My companies are in the UK. And we are creating together a solution, Anti Ransomware. For example, if you are a company and you were attacked by ransomware and they ask it for you for Bitcoins or crypto to recover your information. We use quantum computing and AI to recover your information and you don’t need to pay.

Tim Freestone:

Mmm.

Jean Bernier:

We could not solve the attack today, but we could help you in a few days to recover all your information.

Patrick Spencer:

So you decrypt what they’ve encrypted and you can’t access because they locked it.

Jean Bernier:

Yes.

Patrick Spencer:

Interesting.

Jean Bernier:

We use the pattern finding power of quantum to find the pattern on the ransomware attack.

Patrick Spencer:

And how long does it,

Tim Freestone:

This seems

Patrick Spencer:

you

Tim Freestone:

like.

Patrick Spencer:

know, say Tim’s attack, you know, by ransomware and he calls you up this afternoon. How long before you can have that unlocked and he has access to his data.

Jean Bernier:

Let’s say two weeks, if could take today is in test, is in validation, is a manual process from our side to create the Oracle and support the algorithm. But once the algorithm is running, it’s maybe hours or one day to decrypt information

Patrick Spencer:

process.

Jean Bernier:

because we want to find the pattern. But classic computing. to repeat the pattern and decrypt the files.

Tim Freestone:

So this is, why isn’t everyone using this, Jean? Like, this seems like the ransomware killer.

Patrick Spencer:

They

Tim Freestone:

It

Patrick Spencer:

ought

Tim Freestone:

just

Patrick Spencer:

to be

Tim Freestone:

stops.

Patrick Spencer:

beating your door down. Right? Yeah.

Tim Freestone:

Is it just, is it too, too new? Is it too, um, expense like ransomware? We just saw in the Verizon breach report, social engineering for ransomware is the, you know, the number one threat. And

Patrick Spencer:

24%

Tim Freestone:

if

Patrick Spencer:

of

Tim Freestone:

you

Patrick Spencer:

all threats.

Tim Freestone:

have a way to, yeah, if you have a way to decrypt the files that they’ve encrypted ransomware goes away. So what’s, what’s stopping this?

Jean Bernier:

I think I explained the process that makes a thing that is simple, but creates a quantum algorithm and identifies the right oracle and the right pattern. Well, with quantum, we still work on a linear algebra, so it means many matrix process and you need to work with quantum gates today and

Tim Freestone:

Yeah.

Jean Bernier:

create the oracle and the And let’s say code or algorithm to find the pattern. So that is the complex part. Trying to work with huge amount of data in quantum is still a problem. For example, if you have a spreadsheet file of 50 kilobytes, you could not read today 50 kilobytes as a series and one write in a quantum machine. So. Today, we have many challenges there, and that makes the process that we need to continue growing and is a manual process, but we could make the solutions. Why other people are not making that or other companies? I think there are others working on that. I think I could not be the only one working on that in a world

Tim Freestone:

Yeah.

Jean Bernier:

with a billion people. So many others are working on that. But the difference is if they are sharing that, that they are working on that and making, let’s say marketing message talking about doubt. So I think it could be the difference of the process.

Tim Freestone:

So it sounds like scale complexity and scale isn’t there yet. So you

Jean Bernier:

Yes.

Tim Freestone:

haven’t, yeah.

Patrick Spencer:

If someone wants to contact you, John, in regards to, you know, they’ve had a ransomware attack, they just go to your website. Is that the best way to reach out to you and see if you can help them recover their data? How’s that process work?

Jean Bernier:

Yes, yes, they could find us as spinquantumtech or spinqtech.com or in LinkedIn that is the social network that we use more. So well, we could find there or the AI company is neq.ai, neq with double N and you can find us in Google or other social media too.

Patrick Spencer:

And there’s three use cases that you guys address. So, you know, the encryption capability is one, and then you have these other two, the operational component, and then, you know, expediting or accelerating the AI learning process. Same website. How long are those engagements typically? Like you work with a manufacturer on the operations side, how long is that? Or if you work with someone on the AI front, is that an ongoing engagement, or is it like a two week engagement? What do those resemble typically?

Jean Bernier:

Oh, always is different, Patrick, always is different for any project because we don’t have software as a service or quantum as a service solution today is we create tailor made solutions depending on the challenge of the need of the company. So you need to think if you want to embrace AI or quantum optimization today, at least is a process of four to 10 months. of the project.

Patrick Spencer:

Engagement.

Jean Bernier:

And with the ransomware, it is faster. You could have your solution two weeks, four weeks on the process. And the encryption is working today. That is a solution that we have for take-away.

Patrick Spencer:

they can engage you and off and running right away. Tim, I don’t know about you, but this has been a thought provoking, fascinating conversation. We got to have a follow up call with Jean once you and I have more time to research these subjects and get up to speed on where the market’s headed. Jean, we appreciate your time today.

Tim Freestone:

Yeah, thanks, Sean.

Jean Bernier:

Thanks to you. I enjoyed the conversation. Thanks for the invitation.

Patrick Spencer:

Thanks for our audience. Make sure to check out other Kitecast episodes at kiteworks.com slash Kitecast. Thanks for listening to another Kitecast episode.