With 20 Qubits, the H1-1 Quantum Runs More Complex Algorithm
Our flagship quantum computer, System Model H1-1, is now running on 20 qubits
We sat down with Brian Neyenhuis, Quantinuum’s director of commercial operations to ask him about the 20-qubit upgrade, some of the technical details, and how this launch paves the way for scaling trapped-ion quantum computers in the future.
What are some of the key upgrades made to the H1-1 machine?
The biggest, or maybe the most notable, is that we expanded the number of fully connected qubits from 12 to 20. That is a significant increase and the most qubits we’ve added to an existing machine. Last year, we added two fully connected qubits to the 10 qubits H1-1 already had. It was a major accomplishment at the time. Now, that seems easy compared to this upgrade because for us, it is not as simple as adding qubits.
To add eight more qubits and maintain all-to-all connectivity, we upgraded the optics that deliver the light used to control our qubits. Previously, we were only delivering the light needed to complete quantum gates to three different regions of the trap, which we call gate zones. Now we can address all five zones in our trap simultaneously. This enables us to complete more single-qubit or two-qubit gates in parallel, which means users can run complex algorithms without experiencing a slowdown.
How does this compare to previous hardware upgrades?
This one was significantly more involved than previous upgrades. Although we didn’t modify the trap at the heart of the computer or the vacuum chamber and cryostat that enclose it, we redesigned the entire optical delivery system. This was necessary so as not to deliver light to more regions of the trap, but also to improve stability.
Increasing the size and complexity of the machine without improving the stability would be a recipe for disaster. Because we were able to improve the stability, we were able to add more qubits without sacrificing performance or key features our users expect such as all-to-all connectivity, high single and two-qubit gate fidelities, and mid-circuit measurement.
Why is the increase in zones significant?
The gate zones are where all the interesting quantum stuff happens. More zones allow us to run more quantum operations in parallel, allowing for faster, more complex algorithms.
What's the connection between more zones and more qubits?
Having more gate zones allows us to use more qubits in an efficient way.
Because we can do all these operations in five different locations in parallel, it finally makes sense to put more qubits into the trap. We could have loaded more qubits into earlier versions of the system, but without additional gate zones, it doesn’t make a lot of sense. In fact, doing that would create a bottleneck with qubits waiting for their turn to do a two-qubit gate, which then slows down an algorithm. Now, we can do five quantum gates in parallel, which allows us to run more complex algorithms without sacrificing speed.
Twenty qubits are probably where this generation of traps ends. There is a possibility to add a handful more, but it feels like this is probably the most efficient number for these H1 Systems due to layout of the trap. But future generations, some of which are already trapping ions in the lab today, will use even more qubits and with the same or better efficiency.
What is the “ion dance”?
In the QCCD architecture, trapped ions are easy to move around. By applying the right set of voltages to the trap — a small, electrode-filled device that holds qubits in place — we can arbitrarily rearrange the chain of qubits so any qubit can pair with any other and perform a quantum gate. So, you can think of any algorithm as a set of steps where we shuffle all the qubits to pair them up for the next set of gates, move them into the gate zones, and then shuffle them again to set them up for the next set of gates. The ions “dance” across the trap moving from partner to partner to execute a quantum circuit.
Some circuits, like quantum volume circuits, are densely packed, meaning that every possible pair wants to do a gate at each step of the circuit. Other circuits are very loosely packed, meaning you can only do a few gates in parallel before moving on to the next slice because you need to reuse one of those qubits with a different partner.
Although this dance may sound complicated, it makes it very easy to program our quantum computer. A user sends us a time-ordered set of gates without having to think about the layout of the qubits, and our compiler figures out how to pair up the appropriate qubits to make it happen. You don't have to worry about which ones are next to each other because any pair of qubits is equal to all the others. And, at any step, we can completely rearrange this chain and put any two qubits next to each other.
It’s like a square dance where someone calls out directions to the dancers.
Anything else in the works for Quantinuum’s hardware this year?
We will continue to work with our customers to improve our system performance and their overall experience. One of the reasons we have a commercial system now is to allow our customers to program their algorithms on a real machine. They're dealing with all the constraints of real quantum hardware. They're pushing on their algorithms while we're pushing on the hardware, to get the fastest iterations.
As they learn new things about their algorithm, we learn what the most important things are to improve. And we work on those. We are learning a lot from our customers, and they are learning a lot by running on our hardware.
About Quantinuum
Quantinuum, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
- Mitsui & Co. and Mitsubishi Electric demonstrated one of the world’s largest approximate Quantum Fourier Transforms (QFT) on Quantinuum Helios, scaling from prior records to 98 physical qubits.
- The collaboration also implemented a logical QFT using a QEC (Quantum Error Correction) code with up to 12 logical qubits.
- The work highlights Quantinuum’s accuracy and flexible architecture.
While there is ongoing debate around the pace of quantum computing’s development, a more grounded way to assess progress is through concrete demonstrations of foundational algorithms at meaningful scale. In this context, Mitsui & Co. and Mitsubishi Electric are taking a pragmatic view of quantum progress—focusing on how close the field is to executing core algorithmic primitives that underpin many potential industrial applications, rather than relying on abstract milestones or timelines.
In a new white paper, the industrial giants teamed up with Quantinuum to measure how close we are to running the Quantum Fourier Transform (QFT), a widely-used algorithmic primitive, at scales necessary for industrial applications. In the process, the team successfully ran one of the largest instances of the approximate QFT ever demonstrated. This achievement matters because the QFT is an essential primitive that underpins many of the quantum algorithms expected to deliver practical advantages.
You may have heard of the (classical) Fourier transform (FT), due to its ubiquity throughout modern computing. The FT is essential in everything from image analysis to data compression, with almost limitless applications in between. The quantum Fourier transform (QFT) is similar; it’s used in everything from chemistry to finance.
Because the QFT is a foundational primitive underpinning many quantum algorithms, demonstrating it at larger scales and higher fidelity is a practical way to measure quantum computing readiness. This is exactly the type of benchmarking that organizations should consider to understand where today’s systems are useful, and to see how fault-tolerant approaches are progressing. Ultimately, algorithm-level benchmarking like this is one of the most useful ways to understand not just where we are, but where we are going.
A Transformative Approach
Primitives like Fourier Transform are so widespread because they simplify problems by transforming them into something that is easier to deal with. At Quantinuum, we are very interested in transforms: not only are they crucial for industrial applications but they can also simplify algorithms, making them possible to run now instead of later. This ‘transformational’ approach extends beyond the QFT - other transforms exist, and we have even invented our own quantum-native transforms.
Using our Helios quantum computer and Guppy language, the joint team explored running the QFT on both physical qubits and on logical qubits, showing that fault tolerance is progressing quickly. Running the QFT on 98 physical qubits; the paper shows a clear progression from previous results.
Then, using the Steane code, one of the best-studied quantum error correcting codes, the team used Helios’ 98 physical qubits to form 12 logical qubits, successfully running the QFT with the mechanisms of quantum error correction interwoven into the algorithm. This marks a crucial step forward for the field.
Foundational Progress
Taken together, these results provide a more concrete lens through which to view progress in quantum computing: not as abstract projections, but as measurable advances in the execution of foundational algorithms at increasing scale. By benchmarking the Quantum Fourier Transform on both physical and logical qubits, Mitsui & Co. and Mitsubishi Electric are helping to clarify what today’s hardware can already achieve, and where fault-tolerant approaches begin to extend those limits.
More broadly, the organizations best positioned to benefit from quantum computing will be those that focus on these foundational capabilities early, and use them to build a clear, evidence-based understanding of how the technology fits into their business goals.
It is believed that unlocking answers to some of the most complex scientific and industrial problems will require the seamless integration of high-performance computing (HPC), generative AI (GenAI), and quantum computing. Toward this goal, Quantinuum, NVIDIA, and a major pharmaceutical company have successfully demonstrated the first step in a proof-of-principle framework designed to connect these three distinct computing paradigms for industrially relevant computational chemistry.
This milestone, enabled by three industry leaders and experts in their respective domains, serves as a foundational capability that could support the development of future hybrid quantum-AI workflows to help optimize industrial research and development (R&D).
The potential value is a path toward more automated, repeatable, and scalable workflows for translating chemistry problems into executable quantum programs—capabilities that could eventually make hybrid computing easier to deploy in industrial R&D.
The GenQAI Framework
The framework, termed Generative Quantum AI (GenQAI), involved a quantum computer simulating a pharmaceutical compound using programming instructions generated by an AI model, which itself was trained on quantum data that was simulated using HPC.
While the vision for GenQAI explores how future industrial simulation workflows might be optimized by training AI models using quantum data derived directly from a quantum computer, the framework currently consists of four main technical steps:
- Simulating quantum data: The process began by simulating quantum data with NVIDIA accelerated computing using NVIDIA CUDA-Q.
- Fine-tuning the AI: This simulated quantum data was used to fine-tune a pre-trained AI model from the open NVIDIA Nemotron family.
- Generating instructions: The AI model then generated quantum circuits, which are the programming instructions required for the quantum simulation.
- Validating accuracy: To validate the results, the circuits were run on Quantinuum’s Helios quantum computer using its InQuanto quantum chemistry platform.
The core novelty of this development lies within the process of the framework itself. In this proof-of-principle experiment, an AI model fine-tuned on simulated quantum data generated circuits that were successfully executed and validated on Quantinuum’s Helios system. Rather than delivering an immediate commercial advantage, this achievement establishes a credible, verifiable baseline for how HPC, AI, and quantum computing can function in tandem.
The Case Study
The validation of the framework represents an early step toward the goal of developing scalable architectures for the pharmaceutical industry.
With a shared view toward eventually scaling the framework for pharmaceutical R&D applications, the researchers simulated a pharmaceutical compound: imipramine. This anti-depressant was chosen because it serves as a model compound for drug degradation and shelf-life studies, which are standard components of the pharmaceutical R&D lifecycle.
Developing hybrid infrastructure that enterprises may adopt requires a deep, coordinated effort among domain experts. As such, this successful test highlights the value of combining the strengths of a quantum computing hardware and software leader (Quantinuum), with a hybrid-quantum classical platform (NVIDIA), and a leading enterprise end-user to build and test future computing capabilities for industrial chemistry.
Scaling the Framework
Although demonstrated on a pharmaceutical compound, the architecture could eventually inform similar molecular-simulation workflows in sectors such as energy, agriculture, advanced materials, and electronics. At this stage, it provides a reference for further testing and development.
Engage Further
Access the paper here to explore the full technical details of this demonstration and contact our team to learn more about joining Quantinuum’s enterprise partner network.
Quantinuum is pleased to announce that applications are now open for the Quantinuum SG Grand Challenge 2026, a global innovation challenge designed to bring together researchers, developers, scientists and innovators to explore practical applications of quantum computing.
Organized by Quantinuum and supported by Singapore's National Quantum Office and Aqora, the three-month program aims to foster collaboration across academia, industry and the quantum developer community while supporting the continued growth of Singapore's quantum ecosystem.
A Platform for Quantum Computing Innovation
Participants will work in teams to develop solutions across a range of challenge areas, including chemistry and molecular simulation, optimization, AI for quantum systems, quantum error correction, condensed matter and materials science, and open innovation. Throughout the program, participants will have access to mentoring, technical enablement and Quantinuum quantum computing resources.
Singapore Grand Finale
Selected finalist teams will be invited to present their work at the Grand Finale hosted in Singapore before representatives from academia, and industry. The event will celebrate innovative applications of quantum computing while providing an opportunity for participants to engage with Singapore's growing quantum community.
Join the Challenge
The Quantinuum SG Grand Challenge welcomes participants from around the world. Whether you are an experienced quantum researcher or beginning your quantum computing journey, the program offers an opportunity to collaborate, learn and contribute to the development of practical quantum applications.
Applications are now open. Spaces are limited and subject to review and approval.