基調講演：量子ユーティリティの時代へ

[0:06] Good morning. Let's take a minute to invite all our friends and colleagues to sit down so that we can get started. But first, welcome to the 2023 Quantum Summit. We could not be happier that you have joined us today. My name is Dario Gil. I am a senior vice president at IBM and the director of IBM Research. And wow, what a year 2023 has been from the perspective of technology.

[0:38] Look, computing from AI to semiconductors to quantum computing, it is once again driving markets and birthing industries and really igniting people all over the world. And the imaginations of what we're going to be able to do with technology. And I really believe that we are living in the most exciting times in the world of computing, probably since the advent of either the digital computers in the 1940s or the transistor in the late fifties.

[1:10] And really now with these new technologies, we begin to have an opportunity to start tackling problems that we didn't even think were possible to be addressed with the previous generation of technology. I'm proud to say that this year marks my 20th anniversary at IBM and thank you and I hope you all are getting to see that the company is just so different than it was even five years ago. And this is in no small part because of the foundational work that we carry out in the R&D community inside IBM.

[1:49] And frankly, the rate and pace at which we are commercializing technologies. Look, in my 20 years at IBM, I have never seen us commercialize technology at a faster pace than we're doing it today. We like to say that within the research division that we are the organic growth engine of the company and we're really relentlessly focused on how do we bring the innovations that we're developing in the R&D community and bring them into products.

[2:21] And I highlight as an example of that rate and pace, what we've done this year with AI. I mean, we all know that in the world of AI, you know, as far as the public is concerned, certainly has entered the public imagination of what is possible with that technology. We had become deeply passionate about the role of foundation models for quite a few years inside the research division. But this year we commercialize and created with an intensity that is unlike no other, a new platform to bring generative AI to the world of enterprises and government called watsonX.

[2:56] And we were able to do this in a matter of months. Back to this theme of commercializing technology faster than ever before, and people are really noticing. But of course there is more. There is the world of quantum. And look, I remember reading, you know, a few decades back some of the core ideas around quantum information, science and thinking how powerful those ideas were to become. And if you look at that journey now, they are becoming a reality. And the journey has been amazing.

[3:29] Recall that we made news in the context of the quantum industry in 2016 when we put the first quantum processor on the cloud and within a week we had five times more members that had joins and users than we had expected. And really that marked the moment where quantum computing became accessible to the world. First it showed to hundreds, then to thousands, and eventually to close to half a million people that quantum computing is something to be considered, embraced, and nurtured.

[4:02] Now, in 2017, we introduced Qiskit our Open Source Quantum SDK, and it has become the most widely used software development environment in the world for quantum. In fact, let me give you a statistic. Over 81% of quantum developers prefer it as do over half a million users. That same year we launched the IBM Quantum Network and it exploded. We now have over 280 members today and many of you are here today.

[4:32] So I thank you for that. In 2018, we also established the Quantum Innovation Centers, which have now grown to 38 worldwide. In 2019, we launched IBM Quantum System one. It was the world's first fully integrated and commercially available quantum computer. And since then we've been applying Quantum System Ones all over the world There are now system ones in Germany, Japan and Canada.

[5:05] More are being installed in South Korea, Spain, Japan and here in the US. All this activity is really fantastic and staggering and it is why we have come to call these past few years the era of the emergence of quantum computing. And during this time we have developed more quantum computing power and put it in the hands of more people than anyone else in the industry. Now, it's really been remarkable to see the problems that the community that we build together has been able to tackle.

[5:43] It really hasn't been easy. And but what's made it possible is our philosophy of continued rapid innovation in both hardware and software. In hardware we bought, we brought at least one quantum processor every year with a signature breakthrough. In 2019, we introduced Falcon, a 27 qubit processor. A year later, Hummingbird boasting 65 qubits. And in 2021 we broke the 100 qubit barrier with Eagle.

[6:19] And just last year we introduced Osprey with 433 qubits. And today, as promised, we're announcing Condor with a soaring 1121 qubits. With Condor, we have solved the problem of scaling, of qubit scaling. Now we're taking that knowledge and applying it as we build larger systems. And of course, we didn't stop there.

[6:50] We were also dedicated to improving gate quality and increasing the circuit depth. And that is why as excited as we are about Condor, we're even more excited about Heron. This is Heron, the most performant quantum processor in the world, and the one that will truly scale quantum computing. While Condor removed the roadblock to scale Heron pushes circuit depth and quality with its tunable coupler technology that I'm so proud to introduce today.

[7:29] And wow, if you look at the collective achievements on both on the hardware and the software and the ecosystem, let me give you a statistic that is remarkable. These are the number of circuits that have been executed on IBM Quantum's fleet, over 3 trillion of them. So if we recap what we have accomplished over the last few years, it really has been phenomenal. And in a moment to celebrate and we would say that that phase marked the emergence of quantum computing and that it is time now to move to a new, new era that we're humbly calling the era of utility.

[8:10] Utility is the theme of this quantum summit. And there was one key advancement that we believe marked the transition to this new and exciting era. In June, the team published a breakthrough in scaling quantum computation that was featured on the cover of Nature. The title of the paper is Evidence of Utility Before Full Tolerance in it, our team showed that we can now run large circuits with over 100 qubits and a circuit size of almost 3000 gates and extract noise free estimates from them.

[8:51] So simply put, this goes beyond what you can simulate with brute force. Classical computation and the implications are really huge. And I know so many of you jumped on it. This is what Marked the utility era. And now the community is running experiments in quantum systems today that have both a scale that is large enough to investigate the utility of quantum computing beyond brute force classical computation.

[9:24] And with this new era, we also bring a new system. So it is also my privilege to reveal to you a system like no other in existence a computer with an architecture that is powerful enough, modular and flexible enough to grow with us as we continue to march forward in this journey. So say hello to the IBM Quantum System Two.

[9:54] It is prime cooled and running 100 plus qubit problems just north of us at the TJ Watson Research Center in Yorktown Heights, New York. Look, this is not your typical stand alone system. This is truly a modular system that allows us to connect them into larger, more powerful quantum systems. That's why we call it the building block of quantum centric supercomputing. I truly believe that this era of utility will cement IBM Quantum as an important tool for science and business, and we're bringing this technology to all of you as fast as we're innovating it.

[10:40] But look, a system in the end is only as powerful and as useful as we make it, driven by simplicity. And that is why we are bringing the power of Watson and generative AI to make our quantum systems and Qiskit ever easier to use. I'm showing you here an early prototype, still not quite ready for prime time, but I wanted you to see the future today. Here you see that you can use natural language prompts to generate Qiskit code.

[11:14] And this is just the beginning. Imagine what the platform is going to feel like in the next few years as we bring and we will the full power of AI to our quantum platform. So this is today's IBM, more focused, faster, more open than ever before. And it really is an incredible moment and it feels very different than we've ever done before.

[11:44] Our CEO expresses that IBM is a hybrid cloud, and AI company. But make no mistake, when all of this is said and done, IBM will be a hybrid cloud. AI and Quantum Computing Company, a company that is dedicated to perfecting each of these powerful computing platforms, but also the tools to be able to combine them and extend them to achieve and solve problems beyond what we know is possible.

[12:17] And you, my friends, will be part of that journey and that mission. And on that note, I want to pass it on to the great Jay Gambetta, who's going to come on stage and share with you all the other amazing work and great advancements that have brought us all together today. Thank you. Thank Dario. Thanks, Dario.

[12:47] Good morning, everyone, and welcome to the IBM Quantum Summit. Thanks so much for joining us on our mission to bring useful quantum computing to the world and make the world quantum safe. It's great to see many of our clients and partners here. Without you, IBM Quantum would not be possible. Many of you have been with us for a while, but there are many new faces. So I wanted to take a moment and just acknowledge our new client clients and partners.

[13:19] We welcome quantum computational centers with BasQ, RPI and RIKEN. We also welcome new Quantum Innovation Centers with NQCC, KQC and the University of Copenhagen. Our new commercial partners are EY, Sorry, KPMG, T-systems and SAP and our new industry clients are Banco Bradesco, Dow, Hyundai, Israel Aerospace Industries, Itau, sorry, Moderna, Mitsubishi, Electronic, Pfizer and Truist.

[13:53] Thank you all. So now let's get started. As Dario said, we've entered a new era in the history of quantum computation. And we call this era the era of utility. We've done a lot of important work. We've learned a lot about quantum computing applications, for instance, and simulating nature data with structure, search and optimization. And we as the community have published thousands of papers.

[14:26] I think it's 2.5 thousand papers are being published, but there's a lot more we can do. Until recently, we could run most of the experiments on a laptop as a or as this graph shows. Even a 1981 IBM PC. We need a disruptive change if we want to extract utility from quantum. And earlier this summer we published a paper that shakes this up. We call it the utility paper.

[14:57] We showed that Quantum can tackle problems beyond brute force classical computing methods. And that's what we mean by quantum utility. I think of utility as the first milestone to the road to quantum advantage. We got here thanks to the novel error suppression and error mitigation methods. And in four years, we've been able to increase it by a thousand times. We can now run thousands of gates. And I want you to keep this in mind for the rest of the day.

[15:31] And what's more, in this utility era, we're starting to treat Quantum as the source of truth. And classical methods only serve to verify our results. And don't take my words for this. These are the words of many papers that simulated the results of this nature paper. But what's even more important is this past summer, we've seen at least six more utility experiments that have run quantum circuits with more than 100 qubits and using hundreds or even thousands of gates.

[16:04] These experiments are advancing fields that go beyond quantum computation. They're using quantum computing for quantum computing is now a tool for discovery. Discovering in scientific domains like material science, condensed matter, and particle physics. And we're starting to see this disruptive change. I was talking about. The community is using quantum computers to do things beyond what we can do with the classical simulation.

[16:36] And I hope you'll join this groundswell of disruptive change. You can start using quantum to explore scientific realms that weren't accessible before. I like to say, if you're not using 100 qubits, you're not doing quantum. So I'm going to bring Rajeev to the stage to tell you about what's next in hardware.

[17:13] Thank you, Jay. Good morning, everyone. So I'm going to talk to you a little bit about some of the systems we've put together for this era of utility. All right. So you've probably seen this roadmap that we've talked about in the past that we have been executing on for the past few years. From our 127 Qubit Eagle processor released a couple of years ago to our 433 qubit Osprey system announced last year.

[17:48] We've continued to deliver on these innovations, but I'm really thrilled to share about today is these exciting results from the work we have done on quantum systems this year. And we have two of them to talk about. First, in this area, we've been pushing scale to the limit, and we have done that with Condor, the world's largest quantum computing processor.

[18:19] No claps. All right. But Condor is a result of our exploration into single chip scaling. And fridge capacity, it pushes the limits of how many qubits can be put on a single chip and the ability to yield those qubits. So they work day in and day out, which I can tell you is not a trivial task. With Condor, we asked, what are our challenges that would come fitting over a thousand qubits into one chip and into one fridge, For example, on the left, you'll see how we've added layers of superconducting metal wiring to solve the density problem.

[19:05] And on the right you'll see a key metric of coherence Time is very similar between Condor and Osprey, which was our 433 qubit processor last year. So the learning on scale that we set out to get on this chip is off to a flying start and we're really excited about it. So just to summarize, Condor is our 1121 qubit chip. It features a 50% increase in qubit density and over a mile, a mile, Think about that, of super high density flex IO for signal delivery all fit within a single fridge. Wow.

[19:54] So the technology we put into Condor helped us figure out a key piece of the puzzle to solve the scaling problem. And that's behind us. But from our utility experiments, we know that gate depth and quality matters. And that's where the next step in our journey takes us. Don't get me wrong, we are super excited about Condor, but what we are really, really excited about is Heron.

[20:28] And here it is, my lovely assistant, Jay. They're going to show you a real life heron processor brought here, especially for you. Yes. Thank you. All right. Heron is a 133 qubit chip. Learn from our experience from fixed frequency qubits with a twist, making the couplers tunable to give us the flexibility we need. A little bit of a history lesson. Back in 2019, when we realized we had to do something different, the team really went back to the drawing board to explore new gate architectures.

[21:07] The results that you're about to see reflect the fruits of that labor. This is our first heron chip codenamed Monte-Carlo, and it's already showing significant improvements over our best eagle. It has half the gate error rate and some fidelities in the three nines. It's amazing. But what's more important, we virtually eliminated crosstalk. All right, I'll talk a little bit about it.

[21:39] And it has a significant improvement in gate time. This is going to give our users a huge advantage in the utility era and give us a solid foundation for where we can continue to scale our modular processors. Amazing. And now I'm going to show you over the next few little slides how much heron has really improved. So this is a measurement to assess qubit to qubit crosstalk, right? You'll see two curves on the right, the gray is the isolated qubit operation.

[22:15] And the darker line is where you address multiple qubits. You'll see that our best eagle signal clearly degrades over time. In the multi qubit operation. That's not what we wanted. And now you look at Heron in a very similar operation, it hardly shows any effect, barely a change. This is a big deal. So what does this all mean for our users?

[22:46] All right. The chart shows a metric we believe reflects the value statement of a utility scale processor. We didn't want to really look at just our best qubits. We wanted to look at all our qubits in a chain With Eagle, you can go back two years ago, we started at about 10% error per layered gate. In the last couple of years, we've been able to take that number down to the 2 to 3% range, but that's really not where we want it to be.

[23:18] Coming up, right with Heron, we show a 3x improvement in the error per qubit for these long chains of qubits. This is going to be huge. Now you'll see some difference in the two, the blue and the gray dots that reflect the difference between the 80 qubit chain and 100 qubit chain. Our goal is to get both of them looking the same next to where the gray bar is, and that's what we are in the process of improving.

[23:51] And our next version. Now let's look at performance. Herron's performance again is markedly better than Eagles. That allows us to run a lot more gates enabled by the lower gate time. You see some numbers there. Those are pretty good estimates, but it gives us a very, very clear runway to our goal of 5000 gates and beyond. And you're going to see a lot of that coming for the rest of the day.

[24:22] So how do you put it all together? Right. You have these nice numbers. What does it mean for when the users are using these processors? You'll see Herron is showing real performance improvements, where our users care. You've already heard about the utility paper from Dario and Jay Where we use similar techniques of error mitigation, it shows a 3 to 6x improvement over the eagle we leveraged in that paper. That is huge. This improvement becomes even more noticeable for longer experiments.

[24:59] And now the best part. I'm excited to announce that our first client Heron system called IBM Torino, is now available as an exploratory device in our New York data center. It has a long chain error rate of 100 qubits of less than 0.8%. Our best system ever. We are super excited to see how you use it.

[25:30] Look, this is a great example of four years of research and engineering that will power the rest of our roadmap. We strongly believe we have the right qubits and the right gates to make this a reality. Thanks for listening and we'll turn it over back to Jay. See ya! Yeah Thanks, Rajeev.

[26:01] So the hardware is truly looking great. Some of the gates are as good as three nines and you see our focus is focusing on getting the whole device to work really large. But you're all here because we generally have this tradition of ticking off the roadmap. So to the roadmap. Now we can tick off condor, and heron. If I click the button and we can tick off heron. Now we're in this utility era.

[26:33] The focus has got to be on performance stability and reliability. Utility era means we need our software to support utility scale workloads. And this means we've got to level up Qiskit. I really am excited to bring Jessie to the stage to talk you through what this means Thank you. Thank you Jay. Good morning, everyone. I'm here to tell you about Qiskit.

[27:06] Qiskit has been the de facto standard for creating, optimizing and executing quantum circuits and operators. In fact, Qiskit is the preferred SDK by the vast majority of quantum programmers. Qiskit's success is party because of its write once and run everywhere model That supports most major quantum hardware vendors and architectures, and now we are proud to announce that next year Qiskit will take a major step forward with the release of Qiskit 1.0.

[27:44] This new release will offer even more improvements in performance, stability and reliability. From now forward, we think of running a quantum application with a four-step pattern. And now I want to tell you how Qiskit can help you implement that pattern. Step one is to map problems to quantum circuits and operators. Qiskit has rich toolkits to efficiently construct these circuits and operators, and Qiskit 1.0 has added native support for dynamic circuits such as loops, branches and classical expressions.

[28:22] It has also made significant improvements on memory usage, reducing it by as much as 55% compared to a year ago. Step two is to optimize the inputs. The Qiskit transpiler is an industry leading tool for converting circuits to respect the constraints of the target hardware. In addition, the Qiskit transpiler has a pass manager. that gives you the flexibility to further customize the optimization you want to apply to your circuits and Qiskit 1.0, We see a substantial improvement in both speed and quality for the transpiler.

[29:00] The Qiskit Transpiler is now 16 times faster than a year ago and can generate 23% fewer two-qubit gates. Than another well known toolkit. We also started experimenting, using AI to further improve the transpilation process. This can be easily coupled with the existing Qiskit transpiler. Thanks to the flexibility of its pass manager using a reinforcement learning approach, we've seen a 20 to 50% improvement in both circuit depth and c-not counts compared to heuristic algorithms in Qiskit.

[29:38] The alpha release of this new AI transpiler is available today. And finally, step three is to execute these optimized circuits in primitives. The two primitives we have today sampler and estimater encapsulate the most common queries to a quantum processor. In Qiskit 1.0. We treat their interfaces to make them simpler, more consistent and more efficient in Qiskit runtime, We now support three different execution modes single job, Batch, and session.

[30:17] Running a standalone job was the traditional way of executing quantum circuits on small devices, and it can still be useful for testing and debugging. Last year we introduced sessions which allowed iterative workloads to complete without queuing delays for each iteration. A single session, however, may not be long enough for a real life workload in this utility era. So a session can soon be extended with multiple active windows to accommodate lengthy computations.

[30:52] This year we also added batch, which allowed you to submit multiple non-iterative queries at once. And now with batch execution mode, your workloads can run up to five times faster thanks to parallelism with threading. Thank you everyone. And now in the back to Jay Thanks. There to go.

[31:23] So there's a lot of updates coming to our software. But again, we have to go to the roadmap. What we promised on the Roadmap was threaded primitives. What we've delivered is a suite of execution modes. We call them single job, batch, and sessions. And so with that, we can tick off execution modes. So now that we've entered this new era, people are using quantum computation for doing more tools with discovery.

[31:58] This means we're seeing additional users with additional needs and requirements. And it's my pleasure to bring Paul to the stage to talk about how we're supporting these new users. You Jack. Thanks. Good morning, everyone. So as our hardware and software continue to improve, they open up new opportunities for users to integrate quantum computing into their platform.

[32:33] Having entered the Era of utility has opened up a new set of users that we call quantum computational scientists. Now, a quantum computational scientist is not interested in the quantum hardware itself, but rather in utilizing a machine to solve a distinct computational task. These quantum computational scientists value performance compatibility and ease of use over everything else, and thinking of how to try to satisfy these needs when it comes to quantum algorithms and applications has brought us to the idea of a Qiskit pattern.

[33:05] A Qiskit pattern is the sequence of four steps that all quantum algorithms and applications must follow. In step one, we generate quantum circuits and operators, most likely from classical input data. In step two, we take those quantum objects and we optimize them for execution on quantum hardware. Step three executes our experiment using the Qiskit primitives that we heard about and finally is step for, we post process the primitive output. Okay, now Qiskit patterns are more than just a collection of steps.

[33:36] They provide a logical framework from which we can begin to explore writing algorithms and applications at scale. Right. First, when you think in terms of steps, you highlight the foundational building blocks of which quantum workflows are built and how to leverage those components to create a diverse set of quantum routines. They also allow for containerization. A Qiskit pattern forms an entire quantum program, and we can begin to enhance pre existing workflows with quantum components.

[34:06] And finally, they allow for abstraction away from quantum circuits in operators, alleviating in users from working at the level of quantum assembly code. Okay. To see how this all works, let's take a look at a pattern designed for quantum chemistry. Right. So here it is. You see, again, the four steps were now each Step is comprised of a collection of blocks where each block performs a singular task. These blocks could be made by IBM, third party providers, or even open source contributors. If, for example, we want to change how the circuit and operator in this problem are constructed its a simple exchange of two blocks, leaving the rest of the pattern unchanged.

[34:46] We can take it a step further and we can say let's change the classical optimizer used in step three, and that's also easy to do. However, in this particular example, the optimizer itself is not a building block. This is because Qiskit patterns aims to leverage preexisting software frameworks such as the optimizes from Scipy in this case, and only build the core functionality needed for targeted quantum acceleration. This targeted approach comes in handy when looking at preexisting enterprise workflows, where only a small portion of the of the overall routine is amenable to a quantum solution.

[35:23] Using the same inputs and outputs as the original workflow. A qiskit pattern can be tailored to specific use cases in pipelines, streamlining the development and integration process. And finally, Qiskit patterns are designed to be used at scale. They can be enhanced with information about resource management. Thus optimizing their execution. A pattern can be uploaded to a heterogeneous computing infrastructure such as quantum serverless, allowing for streamlined execution.

[35:55] And finally, our pattern can be run unattended and utilized by end users with no knowledge of quantum computing whatsoever. Okay, so now this is a flavor of where we are going with Qiskit patterns and scalable algorithm and optimization design. And we're going to start rolling these components out beginning next year. However, with Quantum serverless already out in beta, you can begin to prepare for this today. So thank you very much.

[36:29] Thanks Paul So as you see, we're really trying to make quantum frictionless. On the roadmap We said we'd introduce prototype quantum functions and now you see we're starting to define that with what we mean by Qiskit patterns. So to the roadmap to tick off the progress, we now have a prototype way of creating what I envision to be software functions of the future and quantum serverless.

[37:00] So the utility paper has been the main focus of this talk far. But we made another announcement this year. We released a new error correcting code. It's a new low density parity check code that we actually call the gross code. This new code requires orders of magnitude fewer qubits than the surface code, and it scales more efficiently. But implementing this code requires new innovation. We need a new type of coupler to connect qubits that are further apart on the chip.

[37:31] We call these c couplets. It requires more connectivity on our qubits. Degree six. So here's our ultimate goal. We need a system that has c couplers to enable long range quantum connections on the chip in order to implement the code. We need l couplers to create large scale systems and transfer quantum information across the chips. And we need m couplers to transfer information short range between the chips to make bigger chips out of smaller chips.

[38:06] I hope you've seen that We've already proved there's plenty of utility to be had before full tolerance. And now we have a code that that scales better than the surface code, which means error correction is closer than we thought. But we don't currently have a path for error correction on our roadmap. And this means we're going to need a bigger roadmap.

[38:38] Here's our new roadmap. Let's take a tour. Let's take a tour on our development roadmap. It charts the path forward for our client-facing systems and services. Here you see, we are actually showing the number of gates our processors can run in a single circuit for the next five years.

[39:13] Sorry. Here we now show the number of gates that you can run on a single circuit rather than the number of qubits. And for the next five years, we're going to be tackling exactly that quality as opposed to scale. That is because we believe we've solved ssingle chip scaling with the condor. We have the tools we need to build larger systems. The bigger challenge is the tools we need for utility and to continue to improve the quality. So we're putting that front and center.

[39:46] over the next five years We're going to triple the number of gates our processors can run. What's more, Flamingo also includes that quantum communication, and we'll be able to bring at least seven QPU's connected or working together to create a system of thousands of qubits. But then something big happens in 2029. We have Starling. You can see a stark jump all the way to 100 million gates.

[40:18] To me, this is going to be a lot of algorithmic accuracy that we can code into our circuits. Starling is going to be a big a really big deal. It's going to require a lot of new technologies and we're going to take all those technologies and put them into a deployable system. Yes, that's error correction. We're going to say that in 2029 we'll have the first system with error correction.

[40:49] We're also going all the way to 2033 to show you our detailed plans for how we're going to scale to 1 billion gates. But we've realized we need more than a development roadmap. So we're introducing what we call the innovation roadmap. at IBM As you know, we're committed to being transparent about our progress. We want you to trust that we're making progress and we hope that you go on this journey with us.

[41:21] So as I said before, to achieve starling, we're going to need to develop m couplers, L couplers and C couplers. We're going to build the L couplers into Flamingo. We're going to build the M couplers into crossbill and we're going to build the C couplers in the kookaburra. combined with our software innovations this is going to continue us along the path towards useful quantum computing. We're actually putting a lot more in kookaburra. This includes a c couplers, the degrees six couplings, the software for decoding and the list just goes on and on.

[41:51] So I hope you accept that rather than delivering Kookaburra in the planned 2025, we're moving it out to 2026 so we can incorporate all of these effects. Innovations from the roadmap will bubble up into the development roadmap and eventually become available to our clients. The couplers we build with Flamingo, Crossbill and Kookaburra will let us introduce error correction. With starling.

[42:23] And with that, the road is clear to extending quantum utility. Thanks. From our users, we've learned We've learned a lot about the workloads people want to run. And we thank you for this. We now that running a single circuit is not enough. You want to run multiple in parallel and with concurrent classical computations.

[42:54] This is the quantum centric supercomputing that Dario mentioned in his talk. It is driving the vision behind all the updates you saw today. So just to do a recap, one, we have the IBM quantum system two. To me, it looks amazing. It's modular and it'll keep scaling us to the future. And the first one already has three herons inside it, two, heron, our performant system is already making utility workloads run five times better. I can't wait to see those results.

[43:24] Three, with Condor, we've solved single qubit scaling. four with support for parallelization. With the execution modes, batch and iterative workloads are going to run faster. Five, We've simplified quantum algorithms and introduced this concept called Qiskit patterns. Six We've created quantum Serverless and it's in beta. This means it's a much more stable and I'm really looking forward to you seeing it. But all of these were on our roadmap. I hope you also like the additions that were not on our roadmap that we've revealed to you.

[43:56] The first being Qiskit is going to become Qiskit 1.0 And it'll be fast and stable. As Dario mentioned, it's been a wild year in A.I. and we're bringing the full force of A.I. to simplify how quantum computers will be run. And we're introducing ways that we can use, like natural language to create quantum code. To me, this is a big deal. Nine We've actually introduced and showing how we can use A.I. to power coming up with better circuits, and this would be rolled out as a transpiler service in Alpha.

[44:31] You can start using it now. And finally, the 10th announcement is that we now have a ten-year roadmap for all of you that know how much we take the roadmap seriously, Putting a ten-year roadmap is a big deal and putting error correction on it is our commitment to how we're going to bring useful quantum computing to the world. And with that I would like to end by saying we're in the era of quantum utility, just like we said back in 2021, 2023 would be a big year.

[45:02] We have a clear, detailed roadmap for scaling up quantum computing. We've hit all our milestones and now we have systems capable of exploring problems beyond brute force classical computing. Now, I hope you enjoy the rest of the sessions today where you'll see a deep dive on the things I talked about and much, much, much more. The year of quantum utility is here, and I hope you'll come along with us on this journey. Thank you.