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Operator: Good day everyone. And welcome to the IonQ Fourth Quarter and Full Year 2022 Earnings Conference Call. Please also note today’s event is being recorded. At this time, I would like to turn the floor over to Jordan Shapiro, VP FP&A and Head of IR. Please go ahead.

Jordan Shapiro: Good afternoon, everyone, and welcome to IonQ’s fourth quarter and full year 2022 earnings call. My name is Jordan Shapiro and I am the Vice President of Financial Planning & Analysis and Head of Investor Relations here at IonQ. I am pleased to be joined on today’s call by Peter Chapman, IonQ’s President and Chief Executive Officer; Thomas Kramer, our Chief Financial Officer; Dr. Chris Monroe, our Co-Founder and Chief Scientist; and Dr. Jungsang Kim, our Co-Founder and Chief Technology Officer. By now, everyone should have access to the Company’s fourth quarter and full year 2022 earnings press release issued this afternoon, which is available on the Investor Relations section of our website at investors.ionq.com.

Please note that on today’s call management will refer to adjusted EBITDA, which is a non-GAAP financial measure. While the company believes this non-GAAP financial measure provides useful information for investors, the presentation of this information is not intended to be considered in isolation or as a substitute for the financial information presented in accordance with GAAP. You are directed to our press release for reconciliation of adjusted EBITDA to its closest, comparable GAAP measure. During the call, we will discuss our business outlook and make forward-looking statements. These comments are based on our predictions and expectations as of today. Actual events or results could differ materially due to a number of risks and uncertainties, including those mentioned in our most recent filings with the SEC.

Now I will turn it over to Peter Chapman, President and CEO of IonQ. Peter?

Peter Chapman: Thank you, Jordan. Good afternoon everyone. And thank you for joining us for our fourth quarter and full year 2022 earnings call. On a technical and financial level, IonQ had a fantastic year. We believe that we are the leader among the pure-play quantum computing companies and we are highly confident in IonQ having a bright future ahead. For the fourth quarter, we generated $3.8 million of revenue and for the full year we generated $11.1 million. Both revenue numbers exceed the high end of the guidance from our last earnings call. Additionally, for the full year, we secured $24.5 million in bookings, an increase of about 50% from 2021. In a few minutes, I will turn the call over to Thomas to provide more details on our 2022 financials and our 2023 outlook.

Before diving into the updates from this quarter, I want to take a moment to highlight IonQ’s impressive trajectory and current position in the rapidly evolving quantum market. Two years ago in advance of our 2021 public listing, we published an ambitious plan of financial and technical milestones for the future years. Having wrapped up 2022, I am proud to say that we’ve exceeded our original bookings targets in both years and have tracked closely to our technical plans even hitting our technical performance milestone last year, six weeks ahead of schedule. IonQ is trailblazing even in spite of the current market conditions. We have seen current market conditions impact many industries and even some of our peers, but IonQ, we see no signs of slowing down.

Our customers and prospects continue to demonstrate enthusiasm about working with IonQ. Meanwhile, our technical and company-building efforts are moving forward at full speed bolstering IonQ’s position as our front runner in our industry, we believe we are the most well capitalized, pure-play quantum computing company by leaps and bounds. We believe we have enough cash to get the company to profitability based on our current roadmap and our focus is driving to quantum advantage and advancing the development of applications that leverage our technological edge. With that context allow me to share the latest details of our progress at IonQ. First, I’m thrilled to welcome two exceptional leaders who joined our team over the last few months. In late 2022, Rima Alameddine joined IonQ as our Chief Revenue Officer; and Wendy Thomas, the President and CEO of Secureworks, was appointed to the IonQ Board of Directors.

Both Rima and Wendy bring a wealth of expertise and experience and we look forward to the valuable contributions they will make towards IonQ’s continued success. As Chief Revenue Officer, Rima will spearhead IonQ’s expanding sales and revenue division, working with current and future clients to use quantum solutions to solve some of the world’s most complex problems. Rima joins us after nearly seven years at Nvidia as the Vice President of Enterprise Sales for the Americas across multiple industries. Prior to Nvidia, Rima spent 18 years at Cisco where she led the company’s New York enterprise sales business. With Rima at the helm we expect that our go-to-market team will continue to innovate on behalf of customers. As I mentioned, our technical accomplishments were just as impressive as our financial accomplishments in 2022.

Our team successfully executed against our technical roadmap ahead of schedule to deliver our record-breaking system performance of 25 algorithmic qubits by our Q3 earnings call. We continue to believe that IonQ is the only commercial quantum computing company with this superior level of performance, which gives us a head start in enabling real world use cases for customers. What’s more? We have made this technology broadly accessible by offering our IonQ Aria system on Microsoft Azure. We are also experiencing rapid growth in our user base, which has led to a surge of inquiries from potential customers about the transformative potential of quantum computing. At IonQ, we are witnessing the convergence of two powerful trends, the rapid advancement of quantum hardware and the increasing efficiency of quantum algorithms.

This convergence is driving us towards unprecedented advancements in the development of commercially viable applications of quantum computing and we are at the forefront of this transformation. We believe that quantum machine learning or QML will emerge as one of the most robust and practical applications of quantum computing in the coming years. Machine learning involves searching vast amounts of data to find a statistical best fit answer. Quantum computers can be harnessed to efficiently explore many dimensional spaces, even with the small number of variables making them ideal for machine learning tasks. Even with smaller scale systems, quantum machine learning models can be trained to recognize complicated structures more efficiently than classical computers.

We recently announced that our partnership with Hyundai Motor Company to use QML for autonomous vehicles is yielding impressive results. As previously reported by using QML instead of classical ML for road sign detection, we were able to reduce modeling complexity by several orders of magnitude, while maintaining similar accuracy to classical equivalents, even with challenging real life situations such as processing images with low resolution, low contrast, and over exposure. Building on this success, we are now applying similar QML techniques to more complex 3D object detection. We are confident that our QML based autonomous driving solutions can contribute to the development of safer and more reliable transportation technology in the future.

Additionally, on the QML front, you may remember that we provided an update on using QML for natural language processing in our Q3 earnings call well before the recent uptick in dialogue about generative AI. Today we are excited to share the latest work from IonQ, which will be published in the coming weeks in a peer-reviewed academic journal. Our team has successfully designed and run the first ever quantum circuits that can simulate well-known human cognitive biases, such as how the order of questions on a survey impacts the participants’ answers. This work paves the way for new possibilities in natural language processing beyond the capability of classical computers. We envision this bias model becoming a key component in the development of highly effective AI-enabled chatbots in the future.

Our advancements in quantum applications are not constrained to QML. We are also making strides in quantum chemistry. Considering our efforts with Hyundai to optimize battery chemistry, in the past, we shared our quantum simulations for lithium oxide molecules. We were able to successfully run the largest correlated quantum chemistry simulation on quantum hardware to date. The results of this work were shared with the public in the fourth quarter. We believe this work is groundbreaking in the field of quantum computing, demonstrating the immense potential for quantum simulation to tackle some of the most pressing challenges in material science. Today we are collaborating on modeling more complex electrochemical reactions, underlying energy storage mechanisms.

This cutting edge work will facilitate the development of top of the line energy storage solutions that are more efficient, longer lasting, and more sustainable than ever before. In another quantum chemistry initiative, we collaborated with Accenture and the Irish Centre for High-End Computing to create a scalable chemistry simulation platform. Specifically, the tool we built focuses on breaking down human-made carcinogens in the environment. Today’s cutting-edge chemical analysis tools are computationally expensive and limited in scale, restricting the accuracy and scope of compounds that can be studied. By using IonQ quantum computers, which are naturally suited for chemistry simulations, we have a line of sight to cheaper and broader chemical analysis, which could help us to one day eliminate harmful chemicals contributing to environmental pollution.

These applications are all being accomplished with IonQ Aria, a system with 25 algorithmic qubits or AQ. We are already at work on our next milestone of achieving 29 AQ this year, which will allow our technology to tackle greater challenges. For governments, enterprises, developers and investors we believe the time to engage with quantum is today. I mentioned that the convergence of quantum hardware and application development is accelerating timelines for commercial usefulness of our systems. We are firm believers that this trend will continue. It’s this convergence that drove our acquisition of Entangled Networks announced earlier this year. Based in Toronto, Entangled Networks focuses on developing technology that optimizes execution of algorithms on quantum computers.

There are several ways in which we believe the acquisition of the team and assets will accelerate our work at IonQ. In the near term we expect the team’s compiler technology will improve the efficiency of algorithms running on existing IonQ Systems. Secondly, as we begin to build systems with multiple cores on a single chip or multiple chips linked together in a single system, Entangled Networks expertise will help us determine how to optimize algorithms across those configurations and design future hardware architectures. With increasing customer engagements and the prospect of commercially relevant use cases insight, we have been laser focused on improving the manufacturability of our systems. This quarter, we announced plans to build the country’s first dedicated quantum computing manufacturing facility in Bothell, Washington, situated near Seattle.

Our R&D and manufacturing teams will work together here to develop and assemble quantum computers to fulfill projected customer demand, expanding multiple floors and covering 65,000 square feet the new facility is situated in a thriving technology and innovation hub with ample hardware and software talent. It will also serve as our second quantum data center and our first on the West Coast. We announced the plans for this facility with strong public support from the Washington State Congressional Delegation including U.S. Senate President pro temporary Patty Murray support for IonQ’s goal of creating the world’s best quantum computers extends to the highest echelons of the U.S. government. And with that I’ll turn the call over to Thomas for a more detailed review of our financials.

Thomas?

Thomas Kramer: Thank you, Peter, and thank you to everyone joining us today. Let’s walk through this quarters financial results in more detail. As Peter mentioned, we had an excellent quarter recognizing $3.8 million in revenue. For the full year we ended with $11.1 million in revenue. We ended the year with $24.5 million in bookings, which exceeds our original target range for 2022. This also puts us firmly within the range of $23 million to $27 million for the full year that we shared on our Q3 call. Given that we are still at the beginning of our commercialization phase, I want to reiterate my comment from last earnings calls that we expect bookings to continue to be lumpy for quite some time. Moving down the income statement; for the fourth quarter of 2022 our total operating costs and expenses were $27.4 million, up 120% from $12.5 million in the prior year period.

For the full year 2022 that number was $96.9 million, up 138% from $40.8 million in 2021. To break this down further, our research and development costs for the fourth quarter were $13.7 million, up 179% from $4.9 million in the prior year period. For the full year 2022, that number was $44 million up 117% from $20.2 million in 2021. Recall that we are investing heavily in R&D and are increasing our production of our systems to meet projected customer demand. Our sales and marketing costs in the fourth quarter were $2.4 million, up 184% from $849,000 in the prior year period. For the full year 2022 that number was $8.4 million, up 159% from $3.2 million in the full year 2021. This increase was due to us growing our go-to-market function as we continue our investment in our commercialization efforts and we expect that trend to continue as we further expand our sales initiatives.

Our general and administrative costs in the fourth quarter were $9.1 million, up 67% from $5.4 million in the prior year period. For the full year 2022 that number was $36 million, up 162% from $13.7 million in the full year 2021. This increase is largely attributable to a growth in stock based compensation expense, which was $8.9 million for the fourth quarter compared to $1.8 million in the prior year period. Stock based compensation was $31.5 million for the full year 2022, up from $7.7 million in the full year 2021. All of this resulted in the net loss of $18.6 million in the fourth quarter compared to $74.1 million in the prior period, and then net loss of $48.5 million for the full year 2022 versus $106.2 million in 2021. It is important to note that these results include a non-cash gain of $1.8 million for the fourth quarter related to the fair value of warrant liabilities and $30.1 million in non-cash gain for the full year 2022.

We saw an adjusted EBITDA loss for the fourth quarter of $13.3 million compared to an $8 million loss in the prior year period and a loss of $48.7 million for the full year 2022 versus $28.4 million for 2021. Note that we projected an adjusted EBITDA loss for the year of $55 million and have announced $48.7 million in actuals beating our expected plan. Turning now to our balance sheet; cash, cash equivalents and investments as of December 31, 2022 were $537.8 million. We are closely monitoring IonQ’s cash. Our diversified strategy and partnership with several large international banking institutions meant that our exposure to recent events in the banking sector was immaterial. We believe that this approach will continue to serve well in the future.

We are confident in our cash position, which should provide us with sufficient reserves to get to profitability without needing to raise additional funds based on our current roadmap. As Peter mentioned earlier, the quantum market is experiencing rapid growth despite the global market headwinds that are currently impacting much of the technology industry. In the face of these challenging market conditions, many companies have been forced to scale back their financial ions. However, IonQ has remained steadfast in our commitment to delivering on our projections. This unwavering dedication to our goals coupled with our unparalleled expertise in cutting edge technology has positioned us as a leading player in the rapidly expanding quantum market.

Looking forward to our full year 2023 outlook, we are introducing a first quarter revenue target of between $3.6 million and $4 million, and we are projecting revenue of between $18.4 million and $18.8 million for the full 2023 fiscal year. Additionally, we anticipate bookings of between $38 million and $42 million for 2023. Finally, we anticipate an adjusted EBITDA loss of $80.5 million for the full year 2023 at the midpoint of our revenue guidance. These projections demonstrate the strength we see in our product offerings and market positioning. With a clear strategy in place and an exceptional team driving our success, we are confident that we will not only meet these targets, but more importantly pave the way for continued growth and success in the years to come.

And with that, I would like to turn the call back over to Peter for some closing remarks.

Peter Chapman: Thank you, Thomas. To close out, allow me to summarize some of our key takeaways from this call. IonQ continues to establish itself as a leader in the global quantum computing industry. Since going public in October 2021 we have consistently met and exceeded our technical and financial goals, a testament to our visionary leadership team, an unwavering commitment to innovation. We believe we are well on track to continue this trend in 2023 and beyond, further solidifying our position in the market. Our industry leading quantum computers are in high demand with an expanding list of blue chip clients using them to solve real world problems. We take great pride in working alongside these customers to demonstrate the power of quantum computers a testament to the effectiveness of our platform.

Our recent acquisition of Entangled Networks operating assets and the announcement of a new facility in Bothell, Washington marked significant milestones in our pursuit of manufacturing scalability to meet the surging demand of our quantum computers. These developments pave a clear path for IonQ to grow. It’s positioned as a leader in the industry and deliver exceptional performance and value to our clients. As our superior Trapped Ion approach continues to prove its worth we have witnessed a decrease in the number of true competitors to our systems. This is a testament to the unmatched superiority of our technology and the dedication of our team to driving innovation in the quantum computing industry further cementing IonQ’s status as a leader in the field.

And with that I’d like to turn the call over to the operator to begin Q&A.

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Operator: Our first question today comes from Quinn Bolton from Needham & Company. Please go ahead with your question.

Quinn Bolton: Hey guys, congratulations on the strong financial and technical results in 2022 and encouraging to see the continued growth in 2023. I guess maybe first Peter, you talked about the competitive landscape perhaps becoming a little bit more favorable to you as you see some of the smaller competitors becoming less competitive. I guess one question around superconducting I think some of this larger superconducting competitors have announced higher gate fidelity’s and I was wondering if you could just sort of give us your updated thoughts on who do you really see as your competition at this point? Is it superconducting, is it continuum? How do you view that landscape?

Peter Chapman: As a great question Quinn. I would say one, you have to be very careful about how you read some of the superconducting, sometimes people say they’ve achieved a certain fidelity but it means that it’s for sometimes they don’t use the words average two cubic gate fidelity which is what we do. And so in that sense they might have a pair of cubics which do really well, but that doesn’t mean all of them are doing that. And that’s what you really need to do. So today I think most of the fidelity’s are lagging quite a bit behind Ion Trap technology by several years. And in fact actually some of the more recent announcements, I think we started to see people saying superconducting is still five or 10 years away from kind of really getting to stride. So it’s €“ and if you can’t get to 99% average two cubic gate fidelity you probably can’t get to error correction. So that’s usually a still an uphill climb for most of the superconducting folks.

Quinn Bolton: Got it. Okay. And then for Thomas, I guess you’re looking at the 2023 revenue and booking seemed to be sort of right in line with consensus estimates but the EBITDA loss appears to be perhaps a little higher and so wondering if you might be able to talk about what you’re looking at in terms of the OpEx? Are you sort of accelerating some of your OpEx relative to previous plans just to drive the technology roadmap faster?

Thomas Kramer: So you are absolutely right that we are accelerating some of our expenses and that has to do with meeting future demand. As you are aware we’re building our production facility up in Seattle and that we are actually pulling in roughly a year earlier than we thought. And that is precisely because we are seeing a lot of interest in actually buying entire systems, which require us to be able to deliver very consistent units to spec that can be operated without having IonQ personnel on place to actually do it for them. And so that is an expense we knew we were going to have anyway, but we’re doing it now because we see that the market is demanding it.

Quinn Bolton: And I guess that would sort of leads my final question then I’ll jump back in the queue. I think previously you had sort of thought that the company would be in a position to sell a complete quantum computer within 12 to 24 months or sort of by the end of calendar 2024. Any updates on those thoughts?

Peter Chapman: We continue to think that that is exactly what will happen. In fact, I’m more convinced of that than I’ve ever been before because we continue to have new interested parties and I would say, look we should all realize that there are long sales cycles when we talk about selling systems at retail at multiple tens or millions of dollars. So it’s not something that I can pinpoint exactly when will happen, but I’m very confident that it will.

Quinn Bolton: Excellent. Thank you very much.

Peter Chapman: Thank you.

Operator: And our next question comes from David Williams from Benchmark. Please go ahead with your question.

David Williams: Hey, good evening and congrats on the progress, definitely great to see.

Peter Chapman: Thanks David.

David Williams: I guess Peter for you first and you talk a little bit about the manufacturing of systems and just wondering if you could kind of talk maybe through your software stack and how you’re thinking about kind of the, from the user perspective and how you’re simplifying that process. If you have the machine, clearly you’re going to have to need to how to run it. So what are you doing on that front and any developments there?

Peter Chapman: Well it’s on multiple fronts. So on one hand the acquisition that we just did was in terms of the compiler itself that allows people to run more complicated applications on existing work, taking better advantage of the hardware itself, and that’s one of the reasons that we did it. There’s still a lot of work to be done in terms of kind of making quantum available to what I would say is kind of average developers or line of business developer, and that’s still work ahead of us. Today people program in quantum logic gates and I don’t think that that’s the way that people will be programming in quantum five years from now. So it is an area of active interest for us. You can see things to that might be kind of breadcrumbs you’re seeing even in kind of classical software development companies like OpenAI, which are doing automatic code generation. I would hope that those are the kinds of things that come to quantum in the future.

David Williams: Okay very helpful. And one thing I wanted to ask about is that the Dell partnership that you announced, I believe it was last quarter, but we were surprised at Super Compute this year, just kind of seeing that that on display and the kiosk that Dell dedicated to that. Just wondering if there’s any update there or if you can give us any further color on that relationship and kind of how you’re thinking about that and how that really drives the business you think over the next several years?

Peter Chapman: Well as you probably saw and continue to hear from Dell that they think that the combination of quantum and classical is kind of the future. And so they €“ we’ve been working with them on the software and also the hardware integration between the two companies so that you can use classical Dell hardware with IonQ quantum hardware. And so but also from a company point of view, it gives us access to Dell’s kind of sales force so that they can sell quantum as well and it’s still early days but we’ve been doing things like training the Dell’s sales force about quantum so that they can help sell quantum to their customers as well. So we expect that partnership to continue to grow over the next several years.

David Williams: Okay, fantastic. And then maybe just from the, the annual bookings guide, it was a bit better than we had anticipated and the revenue as well. But just if you could kind of talk us through what are the areas or applications you’re seeing the greatest traction? You mentioned QML; can you kind of quantify the demand relative maybe some of the other areas and what you’re seeing in terms of commercial adoption in those areas?

Peter Chapman: So I certainly QML is the €“ what I would describe is the leader, especially maybe in the €“ even in the short-term because we have seen already even on some of our older equipment kind of very interesting results that suggest that and I’ll get a little technical that the number of features that you need to train a ML algorithm is several orders of magnitude less than what you would need for a classical ML solution. So you’ve probably heard some of these large language models take hundreds of millions of features. We haven’t gotten to that scale yet, but for the things that we’ve been doing for €“ for instance for image recognition we’ve shown that we can get those same kind of results but with far fewer numbers of features.

So that’s important because that we’re not sure that what we can get to much larger feature sets in classical hardware. And the other thing that’s really interesting on the ML side is that again in a small €“ small study is that the ML models that get created on the quantum computer seem to capture the signal better than you see in a classical system. So some of the work that we’ve done on the financial side for instance with GE research it seemed to do a better job and some of those are the most interesting data points because they might be an outlier and a Black Swan event, and so a classical ML might be missing that. And quantum seems to capture that relationship when it’s creating it; so all of these things are very promising. If it turns out the ML that you can do on our machine means that it can do a better, ML algorithm, not only could you do better kind of ChatGPTs but better advertising or anywhere ML is being used.

So that’s certainly a large promise. Chemistry also is working well, but ultimately for the true promise of chemistry to do simulation in chemistry, we’re doing small molecules today, but of course, you will need a larger quantum computer to do things like cure cancer and those kinds of things. So those are still a little bit ways away. But even in these small molecules, for instance the work that we were doing with Accenture is interesting that was working on PFAS and ways to be able to break down those forever chemicals. So it’s still very interesting work.

David Williams: Okay, fantastic. And just one more quick one, if I may. Anything from the Garfield versus Boxed decision and I know you filed a petition, but anything new on that or what the impact could be?

Peter Chapman: No, we have no updates today on that particular one.

David Williams: Okay, thank you.

Operator: Our next question comes from Richard Shannon from Craig-Hallum. Please go ahead with your question.

Richard Shannon: Well, hi guys, thanks for taking my questions as well. Maybe I will ask you a question on your goal of getting to an AQ of, I think, 29 for this year up from 25 this year. What is the long poll in the tent in terms of achieving that?

Peter Chapman: Well there are a couple of things here. First, is we have multiple systems which are going for that goal. So, that’s the kind of first part to it is we have the systems up and running and so at this point it’s tuning the systems. You can kind of think of it as a high performance race car, car is assembled, but you still €“ there is always work to do to kind of get the actual performance out of it. So that’s what we’re doing right now as we speak.

Richard Shannon: Okay. Let’s hear, a question on the manufacturing facility here. Especially in light of increasing interest pulling forward this capability for people or for customers that are potentially buying whole systems here. When do we kind of see this first stage of capability up and running here? And what kind of annual throughput in terms of number machines do you expect when you get there? And kind of what’s the capability of the current footprint over the long-term?

Peter Chapman: So, as you would expect in terms of building out a building, there is a number of things that one has to go through, permitting, architectural, design, and then actual construction. So, those things are all currently ongoing. Unfortunately, they don’t happen overnight. It does take some time. So we do expect though, to have kind of the first use of the building by the end of this year. And we haven’t given out yet estimates on terms of number of machines or what the throughput is, but this, the goal of this group and this system is really several fold, one to make basically a product, we’re leaving well behind the kind of the research phase and we’re now getting into production. And so, it’s things like making sure that it’s serviceable by a field service organization that we starting to work on faster ways to be able to build the machines and cheaper and for it to take up a smaller footprint.

So all of those things are going on in the production engineering. It’s a different kind of engineering than we’ve done in the past, which is pushing kind of AQ numbers. This is now really about productization.

Richard Shannon: Okay, great. That’s helpful as well. My last question, I’ll jump out of the line here is on the booking guidance for the year. Obviously a nice increase from what you were able to accomplish last year. I guess my question here is understanding kind of the preto analysis of the expected customer base are going to be booking here. We are looking for a number of somewhat larger contracts to fulfil this, or do you see kind of a broader basis, somewhat smaller ones? How would you characterize this, please?

Thomas Kramer: Well, Richard, we don’t do smaller contracts. Well that is sounded . All of our contracts are large, but what we are seeing is that we have potential to do some exceptionally large ones. We just don’t know when they will happen. And the bookings number that we have arrived at is a function of the number of opportunities we have in the funnel with probabilities attached. And so while there is definitely an element of hardware in there we can’t tell like how fast that will happen and how large it will be, but we really look forward to updating you once we have these news available, of course.

Peter Chapman: Thomas, that sounds like a perfect application for a quantum computer.

Thomas Kramer: I can tell you one for cheat .

Peter Chapman: I’ll get back to you on that. We’ll let you know when it’s available. So the application,

Richard Shannon: Okay. And maybe kind of a quick follow-up on this any way that you could characterize as you look at this probability weighted funnel here of how much of this would potentially be from government type organizations, academic institutions versus, ones that may be more commercial in nature.

Jungsang Kim: So while we don’t break out segments, it is true for any revolutionary new technology is that the early demand is driven by government and academics and then it swings to enterprise. But what we are heartened by seeing is that enterprise is already now dipping their toes in the water and working with us to figure out how quantum can be a mainstay for them in the not so distant actually pretty near future.

Peter Chapman: Yes, just maybe a little interesting follow-up. As I mentioned in the beginning here, we came up with roadmaps, both financial and technical for a number of years plan. And if you remember it’s 29 algorithm of qubits this year, 35 next year and after that 64. And at 64, then you can start to do applications which are starting to rival kind of with the best that you can do on supercomputers for certain applications. So we’re really now €“ we have now a good track record. We didn’t have that at the very beginning but now we very much do and in a public forum. So we’re very confident about basically being 2, 2.5 years away at this point for starting to achieve those kinds of results, which I think, is very different stories than what you see from other technologies that are doing in quantum. And so, we’ll add 29 to that story this year, and then it’s off to 35 and then 64.

Richard Shannon: Okay. Perfect. That is all the questions for me. Thanks guys.

Peter Chapman: Thanks Richard.

Operator: Our next question comes from Kevin Garrigan from WestPark Capital. Please go ahead with your question.

Kevin Garrigan: Yes, good afternoon everyone. Let me echo my congrats on the strong results and strong guide. Just a quick question during your prepared remarks, you spoke about a strong 2022, strong 2023, but are you seeing any push outs from potential customers because of a tough macro environment?

Thomas Kramer: No. It’s just the opposite. We’re seeing lots and lots of interest from customers. And we’re heartened also that customers are re-upping the contracts. So, that’s a good sign.

Kevin Garrigan: Okay, got it. That’s great to hear. And then just a quick question probably for either Chris or Jungsang, I wanted to ask about quantum memory and whether quantum memory is important at this stage in development and then is the trapped ions approach either easier or harder when it comes to this aspect or is another approach better?

Peter Chapman: Jungsang do you want to

Jungsang Kim: Sure. Yes, it’s great. Yes, I guess maybe I need to understand a little bit more of the context of quantum memory. In quantum memory, what I would €“ what I would think is historic qubit and then see if we can hold that qubit for a long time. And for that, I think, it’s very hard, even from a fundamental point of view to find a better match than a trapped atomic qubit, including trapped irons. Are you thinking about quantum memory in a different sense? Because we have memories in our current computers distort information while we compute. And for that, I think, we have a very, very compelling technology. Did that answer your question?

Kevin Garrigan: Yes, it did guys . Okay, thank you. Yes, that’s all I have. Thanks guys.

Jungsang Kim: All right.

Operator: And ladies and gentlemen, with that we’ll conclude today’s question-and-answer session. I like to turn the floor back over to management for any closing remarks.

Peter Chapman: Well just thanks everyone today for joining. Also, for everyone that asked questions, we appreciate it. They are all great questions. I’ll just conclude the strong results are the result of obviously the management team, but also all the employees at IonQ and all the hard work, the late nights, long weekends and so, a hardy thank you to our team for making it happen and continuing to innovate on the behalf of customers. So, thank you so much. And thanks for joining today,

Operator: Ladies and gentlemen with that we will conclude today’s conference call and presentation. We thank you for joining. You may now disconnect your lines.