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I continue to be inspired by our team's pioneering efforts to redefine what’s possible through quantum computing. With more than 550 dedicated employees, we’re constantly pushing the boundaries to uncover meaningful applications for this transformative technology.

This week marked one of my proudest moments: the announcement of a joint venture with Al Rabban Capital to accelerate the commercial adoption of quantum technology in Qatar and the Gulf region. This partnership lays the groundwork for up to USD $1 billion in investment from Qatar over the next decade in ҹɫֱ��’s state-of-the-art quantum technologies, co-development of quantum computing applications tailored to regional needs, and workforce development. This collaboration is a major step forward in our strategy to expand our commercial reach through long-term, strategic alliances that foster economic growth in both the U.S. and Qatar.

I had the unique opportunity to attend a business roundtable in Doha with President Trump, U.S. and Qatari policymakers, and other industry leaders. The conversation centered on the importance of U.S.-Qatari relations and the role of shared commercial interests in strengthening that bond.

A recurring theme was innovation in Artificial Intelligence (AI), reinforcing the role that hybrid quantum-classical systems will play in enhancing AI capabilities across sectors. By integrating quantum computing, AI, and high-performance computing, we can unlock powerful new use cases critical to economic growth and national security. 

We also addressed the growing energy demands of AI-powered data centers. Quantum computing offers a potential path forward here, as well. Our H2-1 system has demonstrated an estimated 30,000x reduction in power consumption compared to classical supercomputers, making it a highly efficient tool for solving complex computational challenges.

What struck me most about the conversations in Qatar was the emphasis on cooperation over competition. While quantum is often framed as a race, our partnership with Al Rabban Capital underscores the value of cross-border collaboration. As I noted in a recent co-authored with Honeywell CEO Vimal Kapur, quantum computing isn’t just a technology—it’s a national capability. Countries that lead will shape how it is regulated, protected, and deployed. Our joint venture and this week’s dialogue reaffirm that both the U.S. and Qatar are taking the necessary first steps to lead in this space. Yet much work remains.

I believe we’re witnessing the emergence of a new kind of global alliance—one rooted not just in trade, but in shared technological advancement. Quantum computing holds the promise to unlock innovative solutions that will tackle challenges that have long been beyond reach. Realizing that promise will require visionary leadership, global collaboration, and a bold commitment to shaping the future together.

I was honored to attend today’s roundtable during the President’s State Visit to Qatar and to see our announcement featured as part of that engagement. This milestone reflects a shared commitment by the U.S. and Qatar to strengthen strategic ties, spur bilateral investment in future-defining industries, and foster technological leadership and shared prosperity. 

ҹɫֱ��’s expansion into the Gulf region, starting with Qatar, follows our successful growth in the U.S., U.K., Europe and Indo-Pacific. We will continue working across borders and sectors to accelerate the commercial adoption of quantum computing and realize quantum’s full potential—for the benefit of all!

Details of the JV are available in this link, along with the .

Onward and Upward,
Rajeeb Hazra

Back in 2020, we to increase our Quantum Volume (QV), a measure of computational power, by 10x per year for 5 years. 

Today, we’re pleased to share that we’ve followed through on our commitment: Our System Model H2 has reached a Quantum Volume of 2²³ = 8,388,608, proving not just that we always do what we say, but that our quantum computers are leading the world forward. 

The QV benchmark was developed by IBM to represent a machine’s performance, accounting for things like qubit count, coherence times, qubit connectivity, and error rates. :��

“the higher the Quantum Volume, the higher the potential for exploring solutions to real world problems across industry, government, and research."

Our announcement today is precisely what sets us apart from the competition. No one else has been bold enough to make a similar promise on such a challenging metric – and no one else has ever completed a five-year goal like this.

We chose QV because we believe it’s a great metric. For starters, it’s not gameable, like other metrics in the ecosystem. Also, it brings together all the relevant metrics in the NISQ era for moving towards fault tolerance, such as gate fidelity and connectivity. 

Our path to achieve a QV of over 8 million was led in part by Dr. Charlie Baldwin, who studied under the legendary Ivan H. Deutsch. Dr. Baldwin has made his name as a globally renowned expert in quantum hardware performance over the past decade, and it is because of his leadership that we don’t just claim to be the best, but that we can prove we are the best. 

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Alongside the world’s biggest quantum volume, we have the industry’s . To that point, the table below breaks down the leading commercial specs for each quantum computing architecture. 

We’ve never shied away from benchmarking our machines, because we know the results will be impressive. It is our provably world-leading performance that has enabled us to demonstrate:

As we look ahead to our next generation system, Helios, ҹɫֱ��’s Senior Director of Engineering, Dr. Brian Neyenhuis, reflects: “We finished our five-year commitment to Quantum Volume ahead of schedule, showing that we can do more than just maintain performance while increasing system size. We can improve performance while scaling.” 

Helios’ performance will exceed that of our previous machines, meaning that ҹɫֱ�� will continue to lead in performance while following through on our promises. 

As the undisputed industry leader, we’re racing against no one other than ourselves to deliver higher performance and to better serve our customers.

At the heart of quantum computing’s promise lies the ability to solve problems that are fundamentally out of reach for classical computers. One of the most powerful ways to unlock that promise is through a novel approach we call Generative Quantum AI, or GenQAI. A key element of this approach is the (GQE).

GenQAI is based on a simple but powerful idea: combine the unique capabilities of quantum hardware with the flexibility and intelligence of AI. By using quantum systems to generate data, and then using AI to learn from and guide the generation of more data, we can create a powerful feedback loop that enables breakthroughs in diverse fields.

Unlike classical systems, our quantum processing unit (QPU) produces data that is extremely difficult, if not impossible, to generate classically. That gives us a unique edge: we’re not just feeding an AI more text from the internet; we’re giving it new and valuable data that can’t be obtained anywhere else.

The Search for Ground State Energy

One of the most compelling challenges in quantum chemistry and materials science is computing the properties of a molecule’s ground state. For any given molecule or material, the ground state is its lowest energy configuration. Understanding this state is essential for understanding molecular behavior and designing new drugs or materials.

The problem is that accurately computing this state for anything but the simplest systems is incredibly complicated. You cannot even do it by brute force—testing every possible state and measuring its energy—because  the number of quantum states grows as a double-exponential, making this an ineffective solution. This illustrates the need for an intelligent way to search for the ground state energy and other molecular properties.

That’s where GQE comes in. GQE is a methodology that uses data from our quantum computers to train a transformer. The transformer then proposes promising trial quantum circuits; ones likely to prepare states with low energy. You can think of it as an AI-guided search engine for ground states. The novelty is in how our transformer is trained from scratch using data generated on our hardware.

Here's how it works:

• We start with a batch of trial quantum circuits, which are run on our QPU.
• Each circuit prepares a quantum state, and we measure the energy of that state with respect to the Hamiltonian for each one.
• Those measurements are then fed back into a transformer model (the same architecture behind models like GPT-2) to improve its outputs.
• The transformer generates a new distribution of circuits, biased toward ones that are more likely to find lower energy states.
• We sample a new batch from the distribution, run them on the QPU, and repeat.
• The system learns over time, narrowing in on the true ground state.

To test our system, we tackled a benchmark problem: finding the ground state energy of the hydrogen molecule (H₂). This is a problem with a known solution, which allows us to verify that our setup works as intended. As a result, our GQE system successfully found the ground state to within chemical accuracy.

To our knowledge, we’re the first to solve this problem using a combination of a QPU and a transformer, marking the beginning of a new era in computational chemistry.

The Future of Quantum Chemistry

The idea of using a generative model guided by quantum measurements can be extended to a whole class of problems—from to materials discovery, and potentially, even drug design.

By combining the power of quantum computing and AI we can unlock their unified full power. Our quantum processors can generate rich data that was previously unobtainable. Then, an AI can learn from that data. Together, they can tackle problems neither could solve alone.

This is just the beginning. We’re already looking at applying GQE to more complex molecules—ones that can’t currently be solved with existing methods, and we’re exploring how this methodology could be extended to real-world use cases. This opens many new doors in chemistry, and we are excited to see what comes next.