Domenico Di Mola, Group VP, Service Provider Cloud Automation, Juniper Networks

Quantum Computing and the Paradigm Shift

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Still image of slide with a title reading Example of the power of Quantum Computer, followed by a definition of quantum computing.

Distinguished Speaker Series: Rob Hays on how the paradigm shift to quantum computing will change the world.

Rob Hays is CEO of Atom Computing, a company on a mission to build the most scalable and reliable quantum computers. In this illuminating episode of the Distinguished Speaker Series, he explains what quantum computing is and talks about some of the technologies that companies and researchers are pursuing to bring quantum computing to life. You’ll learn what people can do with quantum computing, and how it will affect companies like Juniper –– and the entire networking industry

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You’ll learn

  • The market opportunity that is available for quantum computing

  • The technology Atom Computing has created, and why it’s distinct and scalable 

  • How quantum computing will supplement classical computing –– not replace it 

Who is this for?

Network Professionals Business Leaders


Domenico Di Mola headshot
Domenico Di Mola
Group VP, Service Provider Cloud Automation, Juniper Networks

Guest speakers

Robert Hays Headshot
Robert Hays
CEO & President, Atom Computing 


0:00 so good morning in the good evening uh juvenile tour welcome to this new session of

0:06 distinguished speaker uh of the j talk series my name is dominic a member of our technical

0:14 community and i am extremely pleased to present rob hayes who is the ceo of atom

0:22 compute just to introduce briefly who is wrong

0:27 is recognizing this industry leader and the founding of the

0:34 board member of internet alliance and is an executive sponsor or

0:39 for creation of a computer excellence lean consortium and last but not least is serving in a

0:45 steering committee of oecd ai compute task force in

0:52 in his past tenure rob has held important and senior position in both

1:00 lenovo and intel where he met our cto

1:07 raj and he'll probably chat a little bit how they come together he has some interesting tips on this one but

1:14 uh introducing the title of the presentation uh introduction to quantum computing uh it brings

1:21 some memories back to my youth when i was studying uh

1:27 computing and computer physics and effectively it was or it's still about

1:33 electron and holes but rob will will help us to this new

1:39 journey of quantum technology where we will discover uh a more uh

1:45 interesting and awesome word that will come from a new particle a new methodology

1:52 on how they effectively are transforming uh this quantum technology our world and specifically what is the huge impact

1:59 that this technology are airing on the computing which is exactly the quantum computer world

2:06 and so uh with the any further ado i would just uh uh

2:13 welcome rob on the stage and please uh to you

2:18 thank you so much domenico pleased to be here and thanks raj for the invitation to join you guys today

2:24 i actually i met raj it's probably been maybe 15 years or so when i was at intel and he was a fairly

2:30 senior executive as an intel fellow and i was a individual contributor product manager at the time in our ethernet

2:36 division and i was working on some remote management and security technologies that raj had a particular

2:42 interest in so we we became friends and he became somewhat of an informal mentor to me at the time and

2:47 i later i later moved on in my career to be a vice president intel and lead the xeon processor product line

2:53 um as well as other ethernet products and high performance compute fabrics as an executive there and then i went to

2:59 lenovo as the chief strategy officer and all through the way raj and i have kept in touch and found

3:05 ways for our companies to collaborate wherever he's been or i've been and so um pleased to get to speak to the

3:11 juniper team here and um i do have a good mix of background in both computing

3:16 technologies as server system vendor as well as cpu provider but also in networking

3:23 ethernet products ieee standards and other industry consortius so i've kind of been at the crossroads of compute and

3:30 networking through my career and and have enjoyed enjoyed all of it so right now i'm at adam computing i joined

3:37 adam computing which is a startup quantum computing hardware provider last year in july

3:43 as the ceo i actually got engaged with the company about a year and a half ago as a board member and an advisor to the founder and

3:50 then ceo and uh and really became enamored with

3:55 the both the technology and the market opportunity for quantum computing as well as the technology that atom computing has created so i'm gonna i'm

4:02 gonna talk to you today a little bit about um you know what is quantum computing what

4:07 are some of the different technologies that different companies and academic researchers and so forth are pursuing to

4:12 bring quantum computing to life i'll tell you a little bit more in depth about what we're doing at atom computing

4:18 and why why we think it's distinct and quite scalable and then we'll talk about what people are going to do with quantum

4:24 computers and how it might impact juniper and everyone on the call here and we'll open up for questions so

4:30 that's the agenda for today um and i'll get right into it so adam

4:35 computing is based in berkeley california uh it was founded in 2018 by ben bloom who is standing on my left to

4:42 the right in the photo i'm in the center and jonathan king who is

4:48 kind of over my right shoulder and over one and so they both have phds in physics

4:54 and chemistry respectively and are driving our quantum engineering teams as

4:59 well as our applications teams respectively uh we're kind of known for being the fastest to deliver 100 qubits the qubit

5:05 as we'll talk more about is the fundamental building block of a quantum computer um and the number of qubits

5:11 kind of dictates the size of the machine or the scale of the machine and when we announced our 100 qubit system which was completed last year it

5:19 was tied with google's for the world's largest gate-based quantum computer and we did it in only two years with 20

5:24 million dollars raised at the time so much faster and much cheaper than anyone else had done and that's kind of a

5:30 testament to the technology that we chose additionally our qubits are very high quality

5:35 we've published technical papers that that demonstrate world record coherence times

5:40 which is we'll talk more about that but that's how long a qubit holds the quantum state and so that's very important for the performance of the

5:46 computer um

5:52 you know quantum computing is one of these kind of once in a 50-year paradigm shift in computing performance uh it is

5:58 going to touch many applications and many industry verticals we'll talk more about how it's different

6:03 than classical computing but at the end of the day it won't replace classical computing it'll supplement it in the

6:09 same way like gpus and fpgas and accelerators accelerate workloads off of

6:14 standard cpus quantum computers will offload certain portions of workloads that are calculations that are that are

6:22 amenable to quantum subsystems and and really accelerate the

6:27 performance of many applications we'll talk more about that later

6:34 first of all what is quantum computing right so classical computing as everyone is aware

6:39 is uh we have bits and bits can hold uh one state at any time and it's either on or off zero or one or one or zero right

6:46 so um each bit holds a value of zero one in a quantum system we have these things

6:52 called qubits which is just short for quantum bits and they actually don't hold a zero or a one they hold a very cons they have a

6:58 complex coefficient value it's represented as a vector in three-dimensional space uh

7:05 often depicted as a sphere is like what you see in the in the diagram here but each

7:10 qubit holds uh with that one complex number which has a lot more information than just a one or a zero on or off

7:18 and when you string multiple bits or multiple qubits together this is where the power of quantum computing really

7:24 shines so in a classical system again each bit holds a zero one and you can string you know as many of them together

7:30 as you want in your system uh and you can hold one value and that value can represent a a number from zero

7:38 to two to the n minus one right so you put these bits together you get one one larger number as you add

7:43 more bits to it in a quantum system remember we already have complex numbers held in each

7:49 quantum bit and we have a property called entanglement and when you get multiple qubits together

7:55 they're able to what's called entangle and that system can now hold two to the n values at the same time so instead of

8:02 one value from zero to two to the n minus one you can hold two to the n values which means that these cube the

8:08 performance and the power of these qubits grows exponentially with the system which is where quantum computing gets its power from

8:16 to give you one example this is a google example a fairly famous paper they put out about a year

8:21 or so ago where they claimed quantum advantage on a given workload it was a

8:26 workload that is one that's well known for in quantum computing to be something that quantum computers can do

8:32 much faster than classical computers could do and they demonstrated on their

8:37 system which is a 100 qubit system that they can do the calculation in four minutes uh and that calculation would be

8:44 estimated to take about ten thousand years to perform on a classical computer so that's an estimate i don't know if it's

8:50 exactly right or not but you can see that we're talking orders and orders of magnitude different

8:55 performance and so i think that's really kind of a good example of why people are so interested in quantum

9:01 computers is because there's certain calculations that just aren't practical or cost effective to do on a classical

9:08 system so people just won't try them but they can be done on quantum systems um and this is like modeling natural

9:15 systems like fluid dynamics or chemistry molecules

9:21 energy and things like that are are things that people are very interested in modeling on quantum systems

9:29 another kind of comparison this is the summit supercomputer at oak ridge national lab

9:34 this is right now it's the second most powerful quantum computer in the world uh it it as you guys probably all know

9:41 uh super computers are very large this one is the size of two tennis courts they're often the size of a you know a full warehouse

9:47 um this one consumes 10 000 power of 10 000 homes 185 miles of cables 200 million costs uh for for an oak ridge to

9:55 buy this from ibm uh and it the performance you're getting out of this is the equivalent

10:01 performance of what you can get out of 50 to 60 logical qubits a logical qubit is an error-corrected

10:07 qubit um we'll talk a little bit more about that as well but um you can see that you know massive

10:13 performance out of relatively small systems is the promise of quantum computers but here's the problem

10:19 in order to get that power we need to build large-scale quantum computers meaning more and more qubits and we need to do that with error correction

10:25 and error correction requires more physical qubits than what's called a logical qubit because

10:31 you need to sort of over subscribe the system because these qubits are prone to noise and you have a certain error rate

10:38 and in order to kind of mathematically cancel out that error rate uh you need to kind of over subscribe the number of

10:43 physical cubits to logical qubits and it turns out this isn't like a a a ten to five ratio or ten to four ratio

10:50 or something like that it's like hundreds to one a kind of ratio to to really be able to cancel out all the air

10:55 corrections for very large circuits for all the errors and so there's multiple factors that go

11:02 into uh the performance of a quantum computer it's not just the size of the qubit or sorry the number of qubits in

11:08 the system but it's the error rate like i just described so people talk about fidelities this is a very common thing

11:14 for people to report as they say have 99 fidelity or 99.5 percent fidelity that's simply one over error rate

11:21 um so if you have one error out of every 100 you know uh calculations performed on a qubit

11:27 that's a 99 fidelity um and if you think about like a circuit depth i'm going to run a series of

11:33 calculations in order if you you know ran 10 calculations in order and you've got one error at every 100 you would be

11:40 99.9 to the 10 or 99.99 to the 10 and that would actually be a pretty high

11:46 probability of an error so that's why we want to have error correction in there um and a low error rate to begin with

11:52 also coherence so uh these quantum systems uh recart depending on what um

11:58 kind of a technology you're using are often kind of prone to falling out of what's called coherence which means they

12:04 lose their quantum state fairly quickly um superconductors which are the initial kind of quantum computer are

12:11 have a very short coherence time that's one of the downsides of them they only can hold the information for microseconds and then it's lost and so

12:18 if you lose the information while performing a calculation then you have to start over and it turns out with error correction

12:25 there's a feedback loop going on in the system where you're trying to detect and correct errors and you need to hold that information for a long time so short

12:31 coherence means difficult error correction long coherence means lots of time to form deep circuits and lots of

12:37 time to perform error correction so um so that's a very important metric

12:43 there's something people talk about is connectivity connectivity is um qubits get entangled i'll tell you i'll have a

12:48 little demo of that in a few minutes here but basically you can have two qubits and they can interact with each other and

12:55 share information during the calculation and um and there's a there's only

13:00 certain qubits in a system that can connect with each other depending on the topology of the system so in our system for example

13:07 only nearest neighbor qubits in in space the ones that are literally like next door to the one that you're using can

13:13 connect to each other other systems you can connect to qubits within a chip but not across multiple chips and things

13:19 like that so all of that has some impact on performance uh error correction you know do you have

13:25 the ability to detect and correct errors can you do mid-circuit measurement where you can actually detect and correct as you're executing through a circuit or

13:31 you can only do it at the end that will affect performance and then finally gate speed or the clock cycle

13:36 and some um some gates or some cubits take longer to perform calculations than others and so that'll all that adds up

13:42 to a multi-dimensional space kind of problem and trade-off space for for performance in the system

13:50 i'm not going to talk about benchmarks today but there's things called quantum volume and other benchmarks that try to

13:55 take all of these things into account and give some kind of numerical representation of the overall performance of a system

14:02 um but right now what i was going to talk a little bit about some of the different technologies or what we call modalities in the industry for um for

14:08 quantum computers the first two uh quantum computing type systems uh i should say i'm only talking

14:15 about gate based systems here uh there's a type of system called an annealer or a simulator um d-wave is the most popular

14:22 or you know kind of oldest company that's been building these this is kind of a different form of quantum computing

14:29 um it's it's useful but it's only useful for a subset of applications and it's not really the type of systems that most

14:35 people are are looking for for the broad set of applications so within the gate-based quantum computers which is the the kind of more

14:42 broadly applicable type system superconductors were the first technology to build um to build qubits

14:48 for these these quantum computers google ibm and righty are the three leaders in this space basically what

14:54 they do is they build chips using semiconductor manufacturing technology these chips hold qubits in there they

15:00 can basically get the electrons into various states on these chips that represent

15:06 the quantum states within a qubit and they have to wire each chip up together and so that they're all

15:12 connected so they can they can uh they can all operate as one system and so you get these these beautiful photos like

15:17 you see on the left here of what ibm calls the golden chandelier um and that's just simply because you've

15:23 got all these chips and these chips have these gold wires connecting them so that they can they can interact with each other

15:28 um the the they've been doing this for about a decade or more um the qubits are relatively high quality they've got the

15:35 noise out they've got the manufacturing kind of dialed in the problem is scaling with this um

15:41 every time you want to add a qubit you have to add another wire so this is a picture of of a system with a few cube few dozen

15:48 qubits on the left you can imagine if you got like 10 000 or a million qubits you'd have a million wires literally

15:54 each of them sending an rf tone down to the qubit um and i don't know how you would you would wire up a million wires you know that

16:01 supercomputer we saw on the other page was probably uh i don't even know 20 or 30 000 nodes probably so that's you know

16:08 185 miles of cable was probably only going to 20 or 30 000 things not a million things so

16:14 so you can imagine the complexity um more recently companies like ion q which went public last year in continuum

16:20 which was a recent merger between honeywell quantum systems and um and cambridge quantum computing out of

16:26 the uk they've been manufacturing these things called trapped ions it's a different type of qubit technology they also they

16:33 make chips but these chips have little um like channels in them uh or voids and

16:39 then they create magnetic fields in the voids and then they load ions which are just charged particles into those voids

16:45 and they get kind of suspended in air these systems also have to be wired up um and connected together they both of

16:52 these types of systems have to be cryogenically refrigerated and dilution refrigerators and as they get bigger

16:58 more wires larger refrigerators bigger and bigger engineering problems so so while both of these modalities

17:05 have very high quality qubits and they've been around for a while they're both very challenged to scale

17:11 and that kind of leads to what we're doing and what many others in the industry are doing so

17:17 there's a number of companies and academic you know institutions looking for a more scalable

17:22 approach so that we can create many more qubits because we ultimately need millions of qubits right in order to to have a few

17:30 thousand logical qubits and and perform very large calculations and so these are kind of the these aren't all

17:37 of them there's a couple others that are kind of more in the research phase but these are the the seven kind of

17:43 um you know probably most prominent alternatives to superconductors and trapped ions

17:49 uh we're pursuing neutral atoms sometimes they're called cold atoms i'll tell you how it works but you know the 30 second version is we basically

17:56 trap atoms in a vacuum chamber with lasers and they get stuck in free space to that laser so they're not moving at

18:03 all and and if they're not moving they're cold by definition so that's that's why they're also called cold

18:08 atoms and we can manipulate the quantum states inside the atoms nucleus using other laser tones

18:15 um photonics is probably the second most promising of these scalable ones or

18:21 furthest along there's a very quiet startup called sciquantum that is pursuing this and

18:28 xanadu out of i think they're in canada is a little bit more vocal but these are two companies and they're basically

18:34 uh using silicon photonics to get photons to act as qubits

18:39 there's a number of others here you can still it's quantum dots that intel's pursuing silicon spin um and others but

18:45 these are all you know they're all different approaches all different technologies different ways of manufacturing systems but all going

18:50 after more scale because they because people generally see that superconductors and trapped ions will

18:56 will hit some kind of a scaling wall um you know in the not too distant future

19:04 um so to dive in a little bit more into the neutral atoms and how that works and what we're doing at atom computing um so

19:11 neutral atoms are simply um atoms in the second column of the periodic table they're

19:16 alkaline earth metals they have what's called a closed shell they're also called neutral because it

19:21 basically means they have the same amount of protons and electrons which basically means that they don't interact

19:27 with anything in the environment they're just very stable um and that stability is what gives them

19:32 the long coherence times we're able to use the nucleus of the of that atom to create spin uh remember

19:39 if i showed you the represent the uh the coefficient inside a qubit with a sphere well we can actually literally rotate

19:45 the the um the nucleus of the atom to to be in wherever we want in space um i'll show you a demo how we trap them

19:52 with optical tweezers but what's really uh beneficial about this is that these these atoms can be very

19:57 close together we we in our system keep them about four microns apart um and uh we we trap them in a grid and

20:05 you can actually see looks like my little gif animation's not working on this one but usually that little

20:12 diagram in the bottom right is kind of moving and you can see the atoms turning on and off as we're running the system but

20:18 basically we trap these atoms in a ray they're four microns apart our current system is 100 cubits so it's 10 by 10.

20:25 um and so that whole um all hundred qubits fit in 36 microns on

20:30 the side right and when we get to a million um qubits we'll be in a 3d array

20:35 100 by 100 by 100 that's a million four microns apart that's still less than half a millimeter on a side to get all

20:41 million qubits so very very small very different than that golden chandelier that you saw from from ibm and google

20:48 um and all of this is controlled wirelessly because we're doing it with lasers and so instead of having to have a wire that

20:54 goes to each atom we just have a point of light that goes to each atom and that's how we control them

20:59 so i'm going to show you kind of like a cartoon like demo to try to make it uh maybe a little bit more clear what we're doing and then we'll talk a little bit

21:05 more about like what what are you going to do with these quantum computers so

21:11 we basically have a system where this isn't working very well

21:18 but my animation might not be working on uh okay well i'll just talk through it and the animation may not work so we have

21:25 atoms the atoms are basically floating around in a vacuum chamber there's nothing in there except for what we put into it which is strontium

21:32 in a gaseous form and what we can shine a specific color of light

21:37 on on the atom and it and it gets stuck in the focal point of that light

21:43 and it just freezes in space it freezes to like four micro kelvin it's just so still it doesn't move

21:48 um shoot the animation's just not working so let me

21:54 um so we shine that beam of light we capture the atoms

22:00 i'm just going to try to get through this um we but we don't just capture one we capture many so we capture it in a grid

22:06 of light so we just have multiple points of light in a grid the atoms are floating around we capture them

22:12 as they cross the beams of light if if we miss one um we can rearrange the atoms we can just steer the beams of

22:18 light over if we want so what we actually have is a system that's a larger array than 10 by 10

22:24 and we capture some you know something less than 100 or 100

22:29 of the atoms in there and then we just rearrange it into a tight fully populated grid uh to get entanglement

22:35 um and then within uh it within the atom we use the nucleus itself to create the

22:41 qubit and the spin apologize the animation's not working um we can create entanglement with

22:48 what's called rydberg states on these atoms so normally the atoms my fingers are kind of representing the electron

22:53 cloud and the orbit around the nucleus we can actually with different colors of light excite the atom so that the

22:59 electrons a cloud actually goes to a different orbital radius

23:06 and that can be calculated and measured so that's uh it's something that we can control and when they get into what's

23:11 called the rydberg state the the electron cloud is very large and so these atoms that normally are quite far apart from each other four microns which

23:18 is very far compared to the size of the atoms themselves um when they get into these rigberg states

23:23 they're actually bigger than that four micron so the energy between the electron clouds can interact with each

23:28 other and that's how we get entanglement and then we can create what's called

23:35 there's a full gate set that's defined it's i think 22 gates that are out there and we can run

23:40 single cubic gates two cubic gates and we can do all that in parallel so this is just representing like the full array

23:47 running different um gates which make up a circuit and then at the end we shine a flash of

23:52 light on it and we can read out the state of each each qubit um and then we can shine another flash of light on it

23:59 and and all of those qubits get reset to their ground state and we can start over and run another circuit so

24:05 again apologize the animation didn't work over zoom for some reason but that's kind of how it works

24:11 if you uh if you want to kind of see it from a system diagram perspective this represents this blue box in the

24:16 middle represents the vacuum chamber it's got little windows in it this is where the qubits are

24:21 we shine different colors of light into it via different different wavelengths of lasers we

24:28 control all that with a rf control system um interestingly uh it looks a lot like a cellular base

24:34 station um it's a micro tca chassis it's got rf you know line cards in it we're

24:39 running software defined radio network over that so we control everything with software and we're just sending different tones of light or tones down

24:45 the down the cables the rf cables to uh to the lasers to to change the light pulses

24:51 and we're just controlling amplitude phase and and uh frequency on that light in order to go do that and where it is in space

24:58 now we read out with a camera it's all this is it's it's focused through a microscope objective so it's a

25:05 kind of a regular high-speed high-resolution camera that's just focused through that microscope so that's how we read out and all of this

25:10 goes um back uh into a rack of standard servers that sits off to the side of the

25:16 system that runs our software stack which would be our schedule our operating system our

25:21 apis our storage and all that kind of stuff so that's kind of like logically what the system looks like uh physically if

25:27 you walked into our lab in berkeley this is what it this is this is it um

25:32 so this this system here is our first it's a prototype it's not a not a product um

25:39 a hundred cubits it's 5 feet wide 12 feet long most of it is an optical table that you

25:45 see in the middle off to the right is the vacuum chamber with the glass cell and the microscope objectives that

25:52 where the qubits reside all that stuff in the middle that looks kind of complex is um

25:58 hand placed optical acoustical devices mirrors prisms things like that little micro motors

26:04 um and all of that is kind of hand placed for maximum flexibility today for the for the prototype so that we could

26:11 experiment and make changes at will moving forward all of that stuff has been cad designed

26:17 and manufactured so it's all kind of fixed to what we want it to be so that takes the

26:23 flexibility out but it makes it much smaller easier to reproduce and higher quality less error prone

26:29 so the ironic thing as the system grows in size of qubits from 100 to 1 000 to 10 000 and beyond this the physical size

26:37 of the system will shrink substantially because all of that hand-placed stuff goes goes away into little manufactured

26:42 um modules basically um

26:48 and this is i guess our version of the golden chandelier where the qubits sit they're in a little glass cell which is

26:53 where the high vacuum is it's two plates of glass with the frame around it

26:58 and it's high vacuum the atoms are in there and the and the lasers and the microscopes are sort of like fixed up

27:05 against that um it's about the size of a silver dollar to give you some perspective and it

27:11 won't change it'll be that size all the way up through millions of qubits so again the size of the system will will

27:17 the quantum part of the system right here will stay the same the the infrastructure will all get smaller

27:23 um and the performance to go up so kind of a little counter-intuitive but pretty cool and that's why this technology is so interesting for scaling is is that it

27:30 you know you don't have to worry about all these wires this operates at room temperature etc

27:35 um and then here's just like a little kind of photo of like how do we how do we see

27:40 the system right we don't actually have people looking at it computers are looking at it but um but the the system

27:45 is just read out and when we read it out the atom is either glowing or not glowing to say what the answer was 0 1

27:51 for each individual qubit and all the complex computation happens during the calculation

27:57 which is pretty interesting let's see

28:03 so what are we gonna do with these computers um there's a lot of workloads that uh

28:10 companies are starting to invest in um commercial workloads uh i talked to

28:15 you know like fortune 500 companies pretty regularly financial services transportation um energy pharma biotech

28:24 and a lot of these companies already have quantum development teams they've hired quantum physicists and

28:30 computer science and data science professionals who have gotten some training in how do you program quantum computers or been trained on the job

28:36 and they're starting to invest in um in thinking about their workloads and how do they kind of evolve from

28:43 maybe their current hpc workloads or their current um you know like monte carlo simulations

28:49 and things like that and take into account the capabilities of quantum computing and so

28:55 they're already starting to buy time on some of the systems that are out there from ibm or ion q or d-wave and

29:02 running experiments learning how to program these systems and getting some feedback

29:07 while their application teams are are starting to write you know algorithms that can eventually run on quantum computers when they reach sufficient

29:14 scale um you know frankly there's not a lot you can do with 100 qubits today from a commercial perspective it's really

29:20 around experimentation and people are looking for you know thousands and more qubits to be very very useful for them

29:26 once you take into account error correction but these are some of the the early use cases so battery development clean

29:31 fertilizer traffic optimization drug development artificial intelligence is a really

29:37 interesting one i feel like one of my slides got missing here but there's actually four classes of workloads um

29:43 that people are looking at uh optimization problems you're trying to optimize a portfolio

29:49 optimize uh multi-path routing like think packages coming from like a fedex packages you've

29:55 got millions of packages coming from different addresses every day going to different addresses and they get picked up by trucks doing loops in

30:00 neighborhoods and put onto bigger trucks and trains and planes and then fan back out to trucks doing loops and

30:06 neighborhoods that's a multi-path routing problem and quantum computing is really good at that

30:11 or it will be really good at that when we can actually put the maps of sufficient scale into the system

30:16 um and so you can imagine that you could do like a daily map optimization rather than having a

30:22 fixed function sort of loop of these trucks and planes and stuff that could save time money uh

30:27 fuel and all that kind of stuff so that's one example financial services like

30:33 risk modeling and portfolio optimization is another one simulation's a big one so simulating

30:39 natural processes like molecules attaching to diseases for drug

30:45 uh discovery and drug design is a good one

30:51 and things like that also encryption and security is also a big one that gets a lot of probably more um

30:57 probably more news than it should but it is a big deal and um and then machine learning

31:02 optimizations is sort of another another big category um so that's kind of my spiel on like

31:09 what you know what we're doing and what others are doing and how we're different um and some taste of what some of the

31:15 early applications are you know if i uh domenico asked me to kind of think about like well what does

31:20 this mean for you at juniper and and two things came to mind um and i invite domenico to comment on this as i uh

31:28 after i sort of describe my thoughts here and then we'll open it up for questions so um the first one that i that is kind of

31:34 front of mind for me is hybrid compute architecture so um i think for about the last decade or

31:41 so hybrid compute architectures have become the norm or the standard both in the

31:46 cloud service providers as well as in like high performance computing uh in the enterprise

31:53 uh and what i mean by hybrid computing is that you just have different kinds of nodes right you have cpu only nodes that

31:58 are maybe running the base application user interfaces and things like that you've got gpu nodes that are that are running um

32:05 you know vector processing workloads you've got maybe some network optimized nodes that are running

32:11 uh you know security software or communicating with the outside world

32:16 and all of these things have sort of been put into a cluster and there's some kind of a cluster controller and

32:21 scheduler that steers the right workloads or portion of the workloads to the right or right nodes within these clusters right

32:27 and i think and this is just a kind of a photo i grabbed from amazon that just kind of describes the current

32:32 architecture and i just tacked a quantum computer in there because honestly i think that's how it's going to be

32:38 we will have quantum computers that get integrated in with these cloud services and or high performance compute clusters

32:46 and certain portions of workloads will be offloaded to that quantum system and the results will be returned and

32:52 integrated back in with the rest of the workflow and you know from a juniper perspective uh

32:58 you guys are are uh both at the heart of how some of these things

33:03 get connected together within a data center as well as how data centers get connected to the internet and how data gets transferred you know across the

33:09 internet and um you know every time a new architecture gets introduced into one of

33:15 these hybrid compute nodes there's opportunity for network optimization for bandwidth

33:20 latency you know quality service control security and all the rest of that so i

33:26 think it would be great if juniper could think about how to

33:31 uh integrate all of these different subsystems with quantum subsystems in the future to make sure that

33:38 everything works well together um and is secure and then the other thought sort of on

33:45 the right is quantum computing is great and that's what my company is focused on and where my personal interest is but there's

33:52 other quantum physics applications and sensing networking and

33:57 communications and encryption are the three probably biggest ones and arguably will be deployed before

34:03 quantum computing quantum sensing's already deployed to a large extent in the military for

34:10 navigation for aircraft and submarines and and

34:16 and ships and uh and that's probably the earliest market for you know for global

34:22 positioning systems and all that for quantum technologies uh quantum networking is another one and this is

34:27 how do you uh transfer information or states uh you know

34:33 quote-unquote instantaneously between two different places in the planet or in the solar system

34:38 um and also how do you connect quantum systems together so maybe you don't have to convert from quantum to classical back

34:44 to quantum right we can just stay in a quantum domain and what performance or capabilities might that bring to the

34:49 table and then the third one there is encryption and encryption i think applies to everything you know all the

34:55 above and the space including the classical computing but um you have to think about it both from

35:01 an offensive and a defensive perspective um you know most encryption protocols as

35:06 you guys are probably all aware are based on you know prime factor or you know prime number

35:11 um factoring and things like that and um and this is one thing that quantum computers are known to going to be

35:16 really good at is is how do you find all the factors how do you find all the prime factors of any given number no matter how large the

35:22 number is right so um so we nist is already working on new standards that they think are going to

35:28 take a few years to roll out um that will be more um

35:33 uh defend against you know the the threat that quantum creatures uh um

35:39 you know uh pose to current you know like shah standards and things like that but um also how can we use 

35:46 quantum computers to provide better encryption on the sort of offensive side

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