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˰ researchers make a huge leap forward demonstrating the scalability of the QCCD architecture

Solving the “wiring problem”

March 5, 2024

Quantum computing promises to revolutionize everything from machine learning to drug design – if we can build a computer with enough qubits (and fault-tolerance, which is for a different blog post). The issue of scaling is arguably one of the hardest problems in the field at large: how can we get more qubits, and critically, how can we make all those qubits work the way we need them to? 

A key issue in scaling is called the “wiring problem”. In general, one needs to send control signals to each qubit to perform the necessary operations required for a computation. All extant quantum computers have a hefty number of control signals being sent individually to each qubit. If nothing changes, then as one scales up the number of qubits they would also need to scale up the number of control signals in tandem. This isn’t just impractical (and prohibitively expensive), it also becomes quickly impossible - one can’t physically wire that many signals into a single chip, no matter how delicate their wiring is. The wiring problem is a general problem that all quantum computing companies face, and each architecture will need to find its own solution.

Another key issue in scaling is the “sorting problem” - essentially, you want to be able to move your qubits around so that they can “talk” to each other. While not strictly necessary (for example, superconducting architectures can’t do this), it allows for a much more flexible and robust design – it is the ability to move our qubits around that gives us “all-to-all connectivity”, which bestows a number of advantages such as access to ultra-efficient high density error correcting codes, low-error transversal gates, algorithms for simulating complex problems in physics and chemistry, and more. 

˰ just put a huge dent in the scaling problem with their using a clever approach to minimize the number of signals needed to control the qubits, in a way that doesn’t scale prohibitively with the number of qubits. Specifically, the scheme uses a fixed number of (expensive) analog signals, independent of the number of qubits, plus a single digital input per qubit. Together, this is the minimum amount of information needed for complete motional control. All of this was done with a new trap chip arranged in a 2D grid, uniquely designed to have a perfect balance between the symmetry required to make a uniform trap with the capacity to break the symmetry in a way that gives “direction” (eg left vs right), all while allowing for efficient sorting compared to keeping qubits in a line or a loop. Taken together, this approach solves both the wiring and sorting problems – a remarkable achievement.

Stop-motion ion transport video showing loading an 8-site 2D grid trap with co-wiring and the swap-or-stay primitive operation. Single Yb ions are loaded off screen to the left, and are then transported into the grid top left site and shifted into place with the swap-or-stay primitive until the grid is fully populated. The stop-motion video was collected by segmenting the primitive operation and pausing mid-operation such that Yb fluorescence could be detected with a CMOS camera exposure.

Stop-motion ion transport video showing a chosen sorting operation implemented on an 8-site 2D grid trap with the swap-or-stay primitive. The sort is implemented by discrete choices of swaps or stays between neighboring sites. The numbers shown (indicated by dashed circles) at the beginning and end of the video show the initial and final location of the ions after the sort, e.g. the ion that starts at the top left site ends at the bottom right site. The stop-motion video was collected by segmenting the primitive operation and pausing mid-operation such that Yb fluorescence could be detected with a CMOS camera exposure.

“We are the first company that has designed a trap that can be run with a reasonable number of signals within a framework for a scalable architecture,” said Curtis Volin, Principal R&D Engineer and Scientist.

The team used this new approach to demonstrate qubit transport and sorting with impressive results; demonstrating a swap rate of 2.5 kHz and very low heating. The low heating highlights the quality of the control system, while the swap rate demonstrates the importance of a 2D grid layout – it is much quicker to rearrange qubits on a grid vs qubits in a line or loop. On top of all that, this demonstration was done on three completely separate systems, proving it is not just “hero data” that worked one time on one system, but is instead a reproducible, commercial-quality result. Further underscoring the reproducibility, the data was taken with both Strontium/Barium pairs and Ytterbium/Barium pairs. 

This demonstration is a powerful example of ˰’s commitment and capacity for the full design process from conception to delivery: our team designed a brand-new trap chip that has never been seen before, under strict engineering constraints, successfully fabricated that chip with exquisite quality, then finally demonstrated excellent experimental results on the new system. 

“It’s a heck of a demonstration,” quipped Ian Hoffman, a Lead Physicist at ˰.

About ˰

˰, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. ˰’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, ˰ leads the quantum computing revolution across continents. 

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technical
July 29, 2026
Scaling the Signal: What a Larger QFT Says About Quantum Progress
  • Mitsui & Co. and Mitsubishi Electric demonstrated one of the world’s largest approximate Quantum Fourier Transforms (QFT) on ˰ ˰, 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 ˰’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.

, the industrial giants teamed up with ˰ 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 ˰, 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 ˰ 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 ˰’ 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.

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July 29, 2026
˰ and NVIDIA Validate Generative Quantum AI Framework for Pharmaceutical R&D

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, ˰, 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:

  1. Simulating quantum data: The process began by simulating quantum data with NVIDIA accelerated computing using .
  2. Fine-tuning the AI: This simulated quantum data was used to fine-tune a pre-trained AI model from the open .
  3. Generating instructions: The AI model then generated quantum circuits, which are the programming instructions required for the quantum simulation.
  4. Validating accuracy: To validate the results, the circuits were run on ˰’s ˰ 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 ˰’s ˰ 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 (˰), 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

here to explore the full technical details of this demonstration and contact our team to learn more about joining ˰’s enterprise partner network.

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July 21, 2026
˰ SG Grand Challenge 2026

˰ is pleased to announce that applications are now open for the ˰ 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 ˰ 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 ˰ 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 ˰ 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.

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