D-Wave Quantum Inc. (NYSE:QBTS) Q2 2025 Earnings Call Transcript

D-Wave Quantum Inc. (NYSE:QBTS) Q2 2025 Earnings Call Transcript August 7, 2025

D-Wave Quantum Inc. misses on earnings expectations. Reported EPS is $-0.55 EPS, expectations were $-0.06.

Operator: Good morning, everyone, and welcome to D-Wave’s Second Quarter of Fiscal Year 2025 Earnings Conference Call. Today’s conference call is being recorded. At this time, I would like to turn the call over to [Beth Mathis] of Investor Relations. Please go ahead.

Unidentified Company Representative: Thank you, and good morning. With me today are Dr. Alan Baratz, our Chief Executive Officer; and John Markovich, our Chief Financial Officer. Before we begin, I would like to remind everyone that this call may contain forward-looking statements and should be considered in conjunction with cautionary statements contained in our earnings release and the company’s most recent periodic SEC reports. During today’s call, management will provide certain information that will constitute non-GAAP financial measures under SEC rules, such as non-GAAP gross profit, non-GAAP gross margin and adjusted EBITDA loss and operating metrics such as bookings. Reconciliations to GAAP financial measures and certain additional information are also included in today’s earnings release, which is available on the Investor Relations section of our company’s website at www.dwavequantum.com. I will now hand the call over to Alan.

A – Alan E. Baratz: Good morning, everyone, and thank you for joining us today. I’m once again really excited to share our results for the second quarter of fiscal 2025. Building on the company’s remarkable first quarter results, we continue to see accelerating momentum across the business. Let me now walk you through some key highlights, starting with technical achievements. In May 2025, we announced the general availability of D-Wave’s Advantage2 quantum computer, our most advanced and performance system. The Advantage2 system is a powerful and energy-efficient annealing quantum computer capable of solving computationally complex problems beyond the reach of classical computers. A smaller prototype of this system was used to demonstrate our quantum supremacy result on a real-world materials simulation problem, a first for the industry.

Featuring D-Wave’s most advanced quantum processor to date, the Advantage2 system is commercial grade and built to address real-world use cases in areas such as optimization, material simulation and artificial intelligence. As previously shared, the Advantage2 quantum processors have demonstrated impressive performance gains over the previous Advantage system, including double coherence time for faster time to solution, a 40% increase in energy scale for higher quality solutions and increased qubit connectivity from 15 to 20 way to enable solutions to larger problems. It’s a significant engineering achievement that highlights our progress in scaling quantum technology to meet demands for growing computational processing power while maintaining energy efficiency.

We’re helping customers realize value from quantum computing right now, and the Advantage2 system is an important proof point. We recently announced a new strategic development initiative focused on advanced cryogenic packaging, designed to advance and scale both gate model and annealing quantum processor development. The initiative builds on D-Wave’s technology leadership in cryogenic packaging and will expand our multichip packaging capabilities, equipment and processes. By bolstering D-Wave’s manufacturing efforts with state-of-the-art technology, the company aims to accelerate its development efforts in support of its aggressive product road map on the path to 100,000 qubits. As part of this initiative, D-Wave is leveraging deep expertise and processes at the NASA Jet Propulsion Laboratory, JPL.

Harnessing JPL’s superconducting bump on process, we have demonstrated end-to-end superconducting interconnect between chips, work that we expect will serve as an important foundation for scaling both our annealing and our gate model systems. We are continuing important development work that brings together quantum and AI to explore the synergies and benefits of these complementary technologies. Our aim is to help organizations accelerate the use of annealing quantum computers in a growing set of AI applications. To that end, we recently introduced a collection of developer tools to advance quantum AI and machine learning innovation. First, we launched an open source quantum AI toolkit that provides direct integration between D-Wave’s quantum computers and PyTorch, a production-grade machine learning framework widely used to build and train deep learning models.

Second, we launched a new demonstration that illustrates how developers can use D-Wave’s quantum AI toolkit to generate simple images, reflecting what we believe is a pivotal step in the development of quantum AI capabilities. These tools are making it easier for customers like Japan Tobacco and Triumph to build hybrid quantum classical machine learning applications. Customers are increasingly coming to us to explore how to integrate quantum into AI workflows, and we expect that this will remain a priority development area for us. Last quarter, we discussed the purchase of an Advantage2 system by the Julich Supercomputing Center, an important milestone in our burgeoning on-premises business. Demand for purchasing a system has been high, and we’ve been in discussions with numerous organizations around the world interested in buying a D-Wave quantum computer.

Recently, we announced a strategic relationship with Yonsei University and Incheon Metropolitan City to accelerate the exploration, adoption and usage of quantum computing in South Korea. Under the terms of the memorandum of understanding, the 3 organizations are working together to advance research and talent development for quantum computing to provide access to D-Wave’s quantum computing systems and services and to collaborate on the development of use cases in biotechnology, material science and other areas. In addition, the MOU supports our efforts toward the acquisition of a D-Wave Advantage2 system on site at the Yonsei University International Campus in Songdo, Yeonsu-gu, Incheon. Before turning to commercial updates, I wanted to take a moment to remind everyone of the differences between annealing and gate model, as there still appears to be continued misinformation that we believe is confusing the market.

Annealing and gate model are different types of quantum computing approaches that solve different types of problems. According to peer-reviewed research, gate model quantum computers will not offer advantages for all problem types. Multiple research results have shown that annealing quantum computers outperform and are expected to continue to outperform gate model on optimization problems. So gate model cannot universally solve all problems better than classical. It is also important to understand that annealing is not a niche solution. We believe that annealing quantum computing is well suited to a broad set of problems, including AI machine learning, quantum simulation and business optimization, which is ubiquitous in today’s modern enterprise.

Business optimization problems encompass things like workforce scheduling, production scheduling, resource allocation, vehicle routing and so on. Together, these represent extensive use cases with far-reaching potential. To characterize annealing quantum computing as niche is misleading and ill-informed. Both annealing and gate model quantum computers can solve a broad range of problems and each has its limitations with problems it cannot efficiently solve. Annealing quantum computing is very good at solving optimization problems, which cannot be efficiently solved by gate model. Gate model quantum computers, once commercialized, are expected to be very good at quantum chemistry and 3D fluid dynamics, which cannot be efficiently solved by annealing.

And both systems, both systems can tackle linear algebra and factorization, meaning problems related to AI, machine learning and cryptography. We believe that organizations will need both annealing and gate model systems in order to address their full problem sets. This is why D-Wave is building both types of quantum computers for our customers. Now I’ll turn to commercial updates. In terms of D-Wave’s customer portfolio, we signed a number of new and renewing customer engagements for both commercial and research applications, including E.ON, a European multinational electric utility company; GE Vernova, a global energy company; the National Quantum Computing Center, NTCC, the U.K.’s National Lab for Quantum Computing; Nikon Corporation, a multinational corporation specializing in optics and precision technologies; NTT Data Corp., a multinational IT services and consulting company; NTT DOCOMO, Japan’s leading mobile operator; Sharp Corporation, a multinational electronics company; and the University of Oxford.

We’ve also been working with a Fortune 500 aerospace and defense company. And in Q2, we completed a prioritization of 12 different use cases applicable to their business operations that the customer found challenging to solve using classical optimization techniques. Quantum optimization, powered by annealing quantum computing can deliver value in terms of better and faster solutions. Based on the results of our initial exploratory work with this customer, we have now started building the proof of concept for the first of the use cases with a road map to expand to all of them and market them to additional aerospace companies. In addition, we recently built a quantum hybrid proof of technology with North Wales Police to optimize the deployment of patrol vehicles.

The proof of technology solution was tested against historical data and exceeded the customers’ expectations, meeting target response times for more than 90% of incidents and using just 10 seconds of solve time. We’re encouraged by these initial results and see them as important proof points of quantum hybrid’s potential for law enforcement-related use cases. We’re also seeing growing interest in the Leap Quantum LaunchPad program, which is a 3-month trial that provides access to D-Wave’s quantum computing systems, our Leap real-time Quantum Cloud service and our team of quantum experts for project support. Since its introduction in January of 2025, the LaunchPad program has received more than 1,300 applications spanning business, government and academic institutions.

The program is serving as an important vehicle to attract and fast-track customers into proof-of-concept development and ultimately, application deployments. So to summarize, we are continuing to make steady progress across our business. First, delivering on technical milestones, including the release of our sixth-generation quantum computing and advancing quantum AI development. Second, executing against our go-to-market strategy, including increased discussions for on-premises systems with a variety of interested parties. And third, working closely with customers to develop hybrid quantum applications that are addressing critical organizational problems. With the strongest cash position in our company’s history, we believe that we are very well positioned to explore M&A activity that will propel our business even further and faster while delivering value to customers and shareholders alike.

With that, I’ll hand it over to John to provide a review of our second quarter fiscal year 2025 results. John?

John M. Markovich: Thank you, Alan, and thank you to everyone taking the time to participate in today’s call. In my review of the second quarter and first half results, I will be providing non-GAAP operating metrics, including bookings as well as non-GAAP financial metrics, including non-GAAP gross profit, non-GAAP gross margins and adjusted EBIT loss as we believe these measures improve investors’ ability to evaluate our underlying operating performance. These measures are defined in the tables at the bottom of today’s second quarter earnings press release with the non-GAAP financial measures, for the most part, adjusting for noncash and nonrecurring expenses. Revenue for the second quarter of fiscal 2025 totaled $3.1 million, an increase of about $900,000 or 42% from the second quarter of fiscal 2024 revenue of $2.2 million.

The second quarter revenue includes $1 million in revenue associated with the Advantage2 quantum processing unit upgrade for the annealing system that was installed at the Julich Supercomputing Center in the first quarter of this year. Revenue from the Advantage2 upgrade is recognized using the percentage of completion method, reflecting the timing of installation services that are closely integrated with the QPU or quantum processing unit hardware. We expect that this upgrade will be substantially complete by the end of this year. Bookings for the second quarter totaled $1.3 million, an increase of approximately $600,000 or 92% from the second quarter of 2024 bookings of $700,000. As we have previously mentioned, we are encouraged by an expanding sales pipeline that includes a growing number of large enterprises and well-known logos with a market increase in the size of the average transaction size when compared to a year ago.

Many of these companies are focused on having us build them proof of concepts versus just buying a small amount of QCaaS or quantum compute-as-a-service that translates to incrementally more complex transaction structures. This, in combination with the challenges associated with dealing with substantially larger organizations with multistep and sometimes very rigid procurement processes and documentation requirements has resulted in deals taking longer to close than what we had originally anticipated. Over the last 4 quarters, we had over 100 revenue-generating customers that includes customers in the commercial, government and research sectors and nearly 2 dozen Forbes Global 2000 companies. GAAP gross profit for the second quarter was $2 million, an increase of approximately $600,000 or 42% from the second quarter of fiscal 2024 GAAP gross profit of approximately $1.4 million, with the increase due primarily to the growth in revenue.

Non-GAAP gross profit for the second quarter was $2.2 million, an increase of approximately $600,000 or 39% from the second quarter of fiscal 2024 non-GAAP gross profit of approximately $1.6 million. The difference between GAAP and non-GAAP gross profit and gross margin is limited to noncash stock-based compensation and depreciation expenses that are excluded from the non-GAAP gross profit and gross margin. GAAP gross margin for the second quarter was 63.8%, representing a slight improvement from the second quarter of fiscal 2024 GAAP gross margin of 63.6%. Non-GAAP gross margin for the second quarter was 71.8%, a slight decrease of 1.3% from the second quarter of fiscal 2024 non-GAAP gross margin of 73.1%. Net loss for the second quarter was $167.3 million or $0.55 per share, an increase of $149.5 million or $0.45 per share from the second quarter of fiscal 2024 net loss of $17.8 million or $0.10 a share.

The increase in the net loss was primarily due to $142 million in noncash nonoperating charges related to the remeasurement of the company’s warrant liability as well as realized losses stemming from actual warrant exercises. In extracting the impact of the noncash nonoperating warrant remeasurement and related charges from the GAAP net loss, the adjusted net loss for the second quarter was $25.3 million or $0.08 per share, an increase of $5.3 million and a decrease of $0.04 per share from the fiscal 2024 second quarter adjusted net loss of $20 million or $0.12 per share. Adjusted EBITDA loss for the second quarter was $20 million, an increase of $6.1 million or 44% from the second quarter of fiscal 2024 adjusted EBITDA loss of $13.9 million, with the increase due primarily to higher operating expenses that is reflective of our investments to support our future growth opportunity, partly offset by higher gross profit.

I’ll now address the performance for the first half of the year. D-Wave’s revenue for the 6 months ended June 30 was $18.1 million, an increase of $13.5 million or 289% from revenue of $4.6 million in the 6 months ended June 30, 2024. Bookings for the first half of fiscal 2025 were $2.9 million, a decrease of approximately $400,000 or 13% from bookings of $3.3 million in the first half of fiscal 2024. GAAP gross profit for the first 6 months of fiscal ’25 was $15.9 million, an increase of $12.9 million or 420% from GAAP gross profit of $3 million for the first 6 months of fiscal 2024, with the increase due primarily to the high-margin system sale during the 6 months ended in June. Non-GAAP gross profit for the first 6 months of fiscal ‘ 25 was $16.3 million, an increase of $12.8 million or 367% from the year earlier 6 months non-GAAP gross profit of $3.5 million.

GAAP gross margin for the first half of ’25 was 87.6%, an increase of 22% from the 65.6% GAAP gross margin in the first half of fiscal 2024, with the increase due, again, primarily to the high-margin system sale during the first 6 months — for the 6 months ended in June. Non-GAAP gross margin for the first half of fiscal ’25 was 89.9%, an increase of 14.9% from 75% in the 6 months ended June 30, 2024. Net loss for the 6 months ended June 30, 2025, was $172.8 million or $0.59 per share compared with a net loss of $35.1 million or $0.21 per share for the 6 months ended June 30, ’24. In adjusting the impact of the noncash nonoperating warrant remeasurement and related charges from the GAAP net loss, the adjusted net loss for the 6 months ended June 30 was $34.6 million or $0.12 per share, essentially flat when compared to the adjusted net loss of $34.6 million or $0.21 per share for the 6 months ended June 30, 2024.

Adjusted EBITDA loss for the first half of fiscal ’25 was $26.1 million, a decrease of approximately $700,000 or 3% from the adjusted EBITDA loss of $26.8 million in the first half of 2024, with the improvement due primarily to higher gross profit, partially offset by increased operating expenses. Moving on to the balance sheet and liquidity. As of June 30, D-Wave’s consolidated cash position totaled a record $819.3 million, representing over a 1,900% increase from the fiscal 2024 second quarter consolidated cash balance of $40.9 million and a nearly 170% increase from the immediately prior fiscal 2025 first quarter consolidated cash balance of $304.3 million. During the second quarter of fiscal 2025, we raised over $500 million in equity, including $400 million in gross proceeds from our fourth at-the-market equity program, $99.3 million in net proceeds from the exercise of warrants and $37.8 million in net proceeds from our equity line of credit with Lincoln Park Capital Fund that fulfilled the $150 million commitment that was originally secured in June of 2022.

Subsequent to the end of the quarter, we received an additional $15 million from the exercise of warrants. Lastly, during the quarter, we fully recovered the $1 million investment plus accrued interest that we made in Zapata AI in February of 2024 through a convertible note instrument that we wrote off later that year when Zapata became insolvent. As a result of the magnitude of capital that we have recently raised, in addition to pursuing strategic acquisitions, we are accelerating a number of our key investment initiatives. In the area of research and development, we are investing in superconducting bump bond process as highlighted in Wednesday’s — last Wednesday’s press release and as Alan mentioned earlier. This process will support our multichip processor program on our path towards a 100,000 qubit annealing system.

This process will also support scalable cryogenic control of fluxonium-based gate model technology. We will also be upgrading our superconducting printed circuit board advanced packaging manufacturing operation and increasing the number and frequency of our wafer fabrication runs to support building Advantage3 prototypes as well as continuous improvements to qubit coherence times for both our annealing and gate model architectures. In addition, we will be investing in a number of quantum AI research and development programs. In the area of sales and marketing, we will be expanding the size and geographical footprint of our professional services organization to support growing demand for quantum optimization customer engagements across both commercial and government sectors, including U.S. Defense.

And lastly, in the area of G&A, we will be making further investments in our cybersecurity personnel and infrastructure. For the balance of this year, we expect these incremental investments will result in a quarterly non-GAAP OpEx that is approximately 15% higher than our second quarter actual non-GAAP OpEx. To conclude, as we have previously stated, we believe that D-Wave has the opportunity to be the first independent publicly held quantum computing company to achieve sustained profitability and achieve this milestone with substantially less funding required by any other independent publicly held quantum computing company. Given that we are now fortunate enough to have 10 security analysts covering D-Wave, we will, in the essence of time, ask each analyst to commence the Q&A session with one question, and then we will go back through the queue for additional follow-on questions.

With that, we will now open up the call for questions.

Q&A Session

Operator: [Operator Instructions] Our first question comes from Craig Ellis of B. Riley Securities.

Craig Andrew Ellis: Congratulations on the results in the quarter and technical progress, guys. I wanted to start by following up on the point the company had made about M&A and understand the types of M&A that the company believes would make sense, either items that are more technical in nature, maybe helping to accelerate the gate model side of the business or things that might be more go-to-market oriented? And then what size of M&A makes sense for the company? And finally, related to that, what are we looking at in terms of timing as we pursue that growth angle?

Alan E. Baratz: Craig, thanks for the question. So we have not disclosed our strategy and plans for M&A other than to say that with $800 million in the bank, it has now become a strategic priority for the company. That having been said, over the course of the last several months, we have been spending a fair amount of time developing a strategy and a plan. And it falls into a number of key areas, including some of those that you mentioned. But think about it as really accelerating our R&D and product development activities in a number of key areas, which could be everything from gate model to quantum AI.

Craig Andrew Ellis: And anything on timing there, Alan, whether we’re looking at something that could be 2025 versus 2026?

Alan E. Baratz: It’s hard for me to predict. But what I would say is that our goal would be to start being able to announce acquisitions this year. However, it takes 2 to tango. And so we’ll just have to see how that plays out.

Operator: Our next question comes from David Williams of Benchmark.

David Neil Williams: Congrats on the continued progress here. So maybe if you could speak to the cryogenic news and the importance of that towards your road map and what you think it will bring ultimately as you get that ramped into the road map?

Alan E. Baratz: Yes, David, thank you. This is really important to us. So we’ve talked in the past about how we have significant intellectual property and a real lead in cryogenic control. And by that, I mean the ability to control qubits and control our systems on chip rather than needing to do all the control from room temperature. And now as we are looking to leverage that into the gate model program as well as expanding our annealing processes to much larger numbers of qubits, as we said, 100,000 qubits, we really need to be moving to a multichip solution. And when we start interconnecting chips together in the refrigerator, we need to make sure that, that interconnect is also superconducting and that we can preserve the quantum properties like entanglement across those interconnects.

And so this is key to both scaling our annealing systems as well as developing our gate model systems. And frankly, we made progress in this area much faster than we actually expected it to. The NASA Jet Propulsion Laboratory had some capabilities that we thought looked interesting. We thought it would take a while to get to the point where we could actually kind of evolve that into what we needed. But it actually moved a lot faster than we thought. And so now we’re really starting to build a production capability around that technology to more rapidly drive the multichip annealing processes as well as the gate model system.

Operator: Our next question comes from Troy Jensen of Cantor Fitzgerald.

Troy Donavon Jensen: Gentlemen, I also want to offer my congrats on all the technical milestones here. Maybe for you, Alan, I’d just love to hear more about Advantage2. And I guess a few things kind of all coupled together would be, is South Korea deployment, is that going to be Advantage2 or a number of systems you expect to install by the end of next year and maybe some of the technical improvements in the platform?

Alan E. Baratz: Okay, Troy. So first of all, Advantage2 is a really important system for us in the sense that it was the first system on which we were able to demonstrate true quantum supremacy, specifically the ability to solve a useful real-world problem on a quantum computer that cannot be solved classically, full stop. It is what everybody in the quantum industry aspires to, and we were the first to achieve it, and we achieved it on the Advantage2 system. I will tell you, we tried to get this result on our earlier Advantage system, and we were not able to. It took the increased capability of Advantage2 to be able to perform that computation. And specifically, it required the additional interconnect to more efficiently map the problem into the quantum processor.

And it also took the longer coherence time and the increased energy scale to get the solutions faster and more accurately. So this is a significant advance over Advantage, and we’re really excited about it. And it’s also, I think, driving that increased customer interest that John talked about relative to much larger companies with much larger opportunities that are now engaged with us in a kind of sales cycle process. As far as the number of systems, this falls into 2 categories. One is our Quantum Cloud service. And we have 4 production systems in our Quantum Cloud service today. We don’t really need more than that in the cloud service for relatively near-term revenue growth. We likely will add a couple more systems down the road, but 4 is sufficient for now.

And obviously, all 4 of those will be upgraded to Advantage2. Currently, one of them has been upgraded. Ultimately, all of them will be upgraded. And then there’s the on-premise systems. And obviously, Julich is one of those, and we are already in the process of doing that upgrade, as John pointed out. And then as we sell more on-premises systems, those will be Advantage2 processors. And we said that we’ve got a really good pipeline for sales of systems. We’ve got a second one that we’re closing in on, which is the South Korea. We’ve got another one that’s now starting to kind of emerge as a relatively near-term opportunity and then a pipeline of others. But in the past, I have said in the near term, think more like 1 a year than multiple a year.

I’d still say that, although starting to feel like maybe it could be a little bit more. But the number is still kind of relatively small in the near-term. So that having been said, if you add up everything that I said, we’re talking maybe 6 or 7 Advantage2 systems.

Operator: Our next question comes from Richard Shannon of Craig-Hallum.

Tyler Perry Cucinotta Anderson: This is Tyler Anderson on for Richard Shannon. So could you elaborate on the developer tools that you released? And noting that the problem that you demoed is a classical ML problem, can users leverage higher pixel images for this? And then also for the sake of timing question, you mentioned control right after your bump bonding. Do you have already or plan to have integrated control onto your chiplet such that there isn’t an external control mechanism?

Alan E. Baratz: So Tyler, I will answer your first question to be fair to everybody else because we did say only one question, and then you can go ahead and get back into the queue. With respect to what we announced as far as the AI developer toolkit, which I believe is what you’re referring to, essentially, what we have done is we have introduced the ability to use PyTorch, which is an open source Python-based machine learning platform that’s frankly in fairly wide use for training large language models. And we’ve introduced into that the ability to use a technology that we actually developed within D-Wave, which is called a Discrete Variational Autoencoder. Now I’m not going to get into the technical details of what I mean by that other than to say what this does is allows you to take a data set and map it into a latent space, which is really what machine learning is all about, essentially map it into a machine learning model that can then be used to recreate the data and other things that look like that data set.

But what’s unique about this is that we’re mapping it into a discrete latent space, not a continuous latent space. Machine learning today typically operates on continuous data, but we’re now mapping it into a discrete latency, seeing excellent results with the discrete latent space. But what’s so important about that is that our quantum processors natively work with discrete data, not with continuous data. So what this does is it actually opens up the opportunity for the annealing quantum computer to be the vehicle for creating that discrete latent space or doing the learning, which we ultimately think could deliver better models faster and with lower energy consumption. But that having been said, I also want to point out that this is just the next step on our journey in the area of quantum AI.

And there are a number of other things we are working on today in the lab that take this significantly further than what we’ve announced so far that we’re very excited about as well.

Operator: Our next question comes from Suji Desilva of ROTH Capital.

Suji Desilva: I appreciate all the color thus far on quantum AI. Can you talk, Alan, about the next milestones or activities to watch indicating D-Wave’s progress here?

Alan E. Baratz: In the area of quantum AI?

Suji Desilva: Correct.

Alan E. Baratz: I think the short answer to the question is no because we haven’t yet announced anything beyond what we announced a few days ago. But just to kind of, say, a little bit — follow-up a bit more on what I said a minute ago, there are a number of modern approaches to AI and machine learning model training and inference. Variational autoencoders is one approach, but there are other important things like transformation model — sorry, transformer models and diffusion models. And we’re working with those as well. And we expect that over time, we would be able to deliver a platform that could leverage our quantum systems in support of all of those approaches.

Operator: Our next question comes from Harsh Kumar of Piper Sandler.

Harsh V. Kumar: Congratulations on a lot of progress, Advantage2 and other things that you’re doing. Alan, I wanted to ask you about the toolkit information you provided in your press release. Is it possible that you could have libraries kind of like how NVIDIA does offer to its customers sort of hot finish models that they can — the customers can then take and sort of finish up and customize. Is that sort of the idea? Or is that even possible with the toolkit with demos reference that you mentioned in your press release and commentary?

Alan E. Baratz: So it’s absolutely possible. And in fact, the demo is one simple example of that. However, currently, we are working with customers, and leveraging their data in the application areas that are important to them. For example, Japan Tobacco and Triumph, the 2 that I mentioned a bit earlier, rather than trying to build these out ourselves. The extent to which, a, we take any of that and pull it back into our platform will depend a little bit on the customers and the extent to which we’ve negotiated the ability to be able to do that. And then whether we choose to start pursuing any applications or application templates ourselves is not something that we’ve certainly not announced it. We’ve not talked about it. I think that would require us to bring domain expertise in key problem areas into the company. It’s something we’re thinking about. But honestly, at this point, I wouldn’t say we’re going to do it. I would just say we’re thinking about it.

Operator: Our next question comes from Kingsley Crane of Canaccord Genuity.

William Kingsley Crane: So quantum annealing really has significant potential with both corporates and nation, states or agencies. I’m curious how the tenor in the U.S. government has shifted, specifically with respect to quantum annealing in the past couple of quarters? And then just any thoughts on DARPA’s Quantum Benchmarking Initiative and if there’s an opportunity for annealing within that framework?

Alan E. Baratz: You really know how to push my buttons. Okay. Let’s start with QBI. The DARPA QBI program is totally focused on gate model. And I think that is a huge mistake on the part of DARPA and the U.S. government. I think that by focusing on gate model, they are totally missing the fact that annealing is the most capable approach to quantum for many of the important problems that the government needs to address, whether it’s in the area of defense for things like missile defense or troop resupply or in the area of transportation, for example, things like port logistics or we’ve done work in the area of wildfire fighting. I mean, the truth of the matter is annealing represents, I think, not only the best, but the only quantum approach that can address many of the government’s hard computational problems.

This is back to the fact that many of these are optimization problems, which require annealing quantum. Gate model cannot address them. And by excluding annealing from the QBI program, I think DARPA has made a huge mistake. And I would encourage them to maybe not add annealing to the QBI program, but maybe create a second quantum program for non-gate model approaches to ensure that the U.S. government is kind of focused on all the approaches to quantum computing, not just one approach to quantum computing. Now with respect to progress more broadly in the U.S. government, I’d say the answer is yes, but slow. So we are making inroads into different application areas because we uniquely can do that since we have a system that is capable of delivering value today, not 5 or 10 years from now, but it’s slow going.

Operator: Our next question is coming from Ruben Roy of Stifel.

Ruben Roy: Alan, I don’t mean to push your buttons here, but I’m going to ask this question anyway. So — and I guess it’s in the context of the M&A commentary that you made and also some of the comments that John made with respect to having conversations with larger customers and sort of getting feedback, I guess, from them. So with all of that in mind, I guess the simple question is, has your philosophy on the timing of when D-Wave might think about bringing a gate model to market changed? Has that accelerated for any reason or no? And if no, kind of are customer conversations sort of driving you to think that the time frame that you guys were thinking about previously is probably the right time frame?

Alan E. Baratz: Yes. That’s not pushing my buttons at all. That’s just a great — well, they’re all good questions and they’re all good conversations to have, but I’m not annoyed about gate model and annoyed about DARPA. So relative to our gate model program, look, we still believe that, a, you will never, never see a commercially viable gate model quantum computer before we have error correction. And then we still need to scale to solve useful real-world problems. And as a result, we’re still many years out because no matter what you hear, in the — from the industry, there are still very hard problems that need to be solved around error correction. It’s not just a matter of engineering. And then there are still very hard problems that need to be solved in scaling.

It’s not just a matter of engineering. And so we do think that it’s still a number of years before we will see a scaled error corrected gate model system that is commercially viable. So for us, though, the focus is on removing the risk. In other words, providing clearer line of sight to being able to deliver that by, a, driving the R&D efforts needed and/or possibly bringing in-house really great things that are going on out there in the industry. And so it’s less, I think, about accelerating the time frame and more about kind of being much more concrete on exactly what the road map is that will get us there.

Operator: Our next question is coming from Kevin Garrigan of Rosenblatt Securities.

Kevin Garrigan: I’m going to switch gears a little bit. And you mentioned you signed renewing customer engagements. Can you give us a sense of what your retention rate is? And the customers that do renew, have they typically already had another application in mind that they wanted to use your quantum annealer for? Or does the team show them how else they can benefit from quantum and that gets the renewed engagement?

Alan E. Baratz: Yes. John, do you want to talk about the retention rate, and then I can provide color on kind of how we grow with our customers?

John M. Markovich: Sure. On average, our retention rate going back over approximately like a 4-quarter period is in excess of 90%.

Alan E. Baratz: So we do have a very high retention rate. But we’ve talked a little bit about this in the past. You need to keep in mind that we have 2 different types of quantum compute-as-a-service customers. There are customers that we call do-it-yourself, where they come in and they buy some access, a developer seat and start kind of exploring, doing research, trying to develop applications on their own. And these customers tend to just continue to renew quarter after quarter or year after year, but haven’t been kind of growing or converting from experimentation to actual production applications. And so one of the things we are focused on is how to help them move faster into production. But not all of them are even kind of at the stage where it makes sense to do that.

I mean, some of them are research organizations, which would never convert to production applications. And some of them are smaller organizations that really are just experimenting. So rather, it’s kind of really understanding who those do-it-yourself customers are and focusing on trying to engage them with kind of help to move forward. And in some sense, the Quantum LaunchPad program was put in place in part specifically to do that to move those customers off of just do-it-yourself into the LaunchPad program where it comes with some support from our professional services team. Then the other class of customers is the customers that have engaged us through a professional services engagement. And those are the different customers that, as John said, it’s much larger customers that are engaging us now with much larger projects.

For example, that aerospace company, I mean, a Fortune 500 company engaged us on 12 different applications from the outset. And we did the evaluation and are now working on the first of those applications with a plan to move through all of them and then take it to other aerospace companies. So our approach now really is to work with these larger companies to really kind of find a flagship customer in each industry or vertical area, work with them the way we did with this customer. And then as we’re helping them progress through the applications, take that out to other customers in the same industry and so on.

Operator: Our next question is coming from Craig Ellis of B. Riley Securities.

Craig Andrew Ellis: I wanted to follow-up on my first question but take it in a different direction. So clearly, there’s a lot of business model flexibility you now have with a much higher cash balance. And one of the things you can do is, is organically invest a greater amount in R&D and marketing. What I’d like your help on is understanding how you’re evaluating current intensity versus higher levels and what we should expect is you evaluate where you can go with internal investment to accelerate your path to further commerciality, especially with this growing Quotient Fortune 500 Global 2000 customers in your pipeline.

Alan E. Baratz: Yes. So Craig, first of all, we have in the past said that we are investing in go-to-market. We made a significant, for us, investment in the sales portion of go-to-market sales and technical account management as we over doubled the size of that team in the first half of this year. And now as John said, we’re focused on building out the professional services team in support of that. But we’re also taking it a step at a time. So we’ve made an investment and the pipeline looks good. We’re making progress. It’s taking a little bit longer to get these deals closed than we had expected because of the size of the customer and the complexity of the processes involved, but we’re making good forward progress. And we want to see that we’re getting a return on that go-to-market investment.

And then once that’s been validated, we’ll continue to grow there. On the R&D side, we also are starting to make some additional investments in R&D. We talked about the advanced cryogenic packaging work. We’ve talked a little bit about quantum AI. These are areas where we are starting to make some incremental investments. And beyond cryogenic packaging and its impact on both annealing and gate, there will be incremental investments on the gate model side as well as we continue to kind of work through all the technical elements and R&D elements of that program. And John kind of gave you a metric to think about with respect to an increase in spend throughout the remainder of this year.

Operator: We also have a follow-up question from Richard Shannon of Craig-Hallum.

Tyler Perry Cucinotta Anderson: So I had noticed that Triumph had mentioned using Advantage2 in their upcoming research. Are you in talks with them for a sale of QPU? And is this someone who you have recently been talking to or you have mentioned that you had been talking to about sales?

Alan E. Baratz: So Triumph is actually working with our system today, and they have seen really good results leveraging our system to do basically generative AI around a particle acceleration problem that they’re dealing with. And we’ve talked about them in the past. We’ve talked about the work that they’re doing. I mentioned them a little earlier. So they are a customer. They are working with our system. They are working with our system in the area of generative AI. They have seen some really good results with that, and we’re continuing to grow that relationship with them. As far as the system sale, I haven’t really talked about who is in the pipeline and who we’re engaged with other than we have now begun talking about Yonsei University in South Korea.

Richard Cutts Shannon: Appreciate the color. Congrats.

Operator: [Operator Instructions] There are no further questions at this time. I would now like to turn the call back over to Alan Baratz for his closing remarks. Please go ahead.

Alan E. Baratz: Thank you, operator. So as I think you all know, at D-Wave, our mission is to help customers realize the value of quantum computing right now. Our second quarter results show continued progress in service of that mission across R&D, go-to-market, customer application development and more. Everything we build is designed to provide lasting value for our customers and shareholders, and the future looks very bright. So thank you all for taking the time to join us today.

Operator: Ladies and gentlemen, this concludes today’s conference call. Thank you for your participation. You may now disconnect.