豆包成人版

Skinny Logic: Quantum Codes Go on a Diet

March 4, 2026

In our latest paper, we鈥檝e taken a big step toward large scale fault-tolerant quantum computing, squeezing up to 94 error-detected qubits (and 48 error-corrected qubits) out of just 98 physical qubits, a low-fat encoding that cuts overhead to the bone. With 64 of our logical qubits, we were able to simulate quantum magnetism at a scale that can be exceedingly difficult for classical computers.

The "holy grail" of quantum computing is universal fault-tolerance: the ability to correct errors faster than they occur during any computation. To realize this, we aim to create 鈥渓ogical qubits,鈥 which are groups of entangled physical qubits that share quantum information in a way that protects it. Better protection leads to lower 鈥渓ogical鈥 error rate and greater ability to solve complex problems.

However, it鈥檚 never that easy. An unofficial law of physics is 鈥渢here鈥檚 no such thing as a free lunch鈥. Creating high quality, low error-rate logical qubits often costs many physical qubits, thus reducing the size of calculations you can run, despite your new, lower-than-ever error rates.

With our , we are thrilled to announce that we have hit a key milestone on the 豆包成人版 roadmap: an ultra-efficient method for creating logical qubits, extracting a whopping 48 error-corrected and 64 error-detected logical qubits out of just 98 physical qubits. Our logical qubits boasted better than 鈥渂reak-even鈥 fidelity, beating their physical counterparts with lower error rates on several different fronts. And still that isn鈥檛 the end of the story: we used our 64 error-detected logical qubits in a large-scale quantum magnetism simulation, laying the groundwork for future studies of exotic interactions in materials.

Stacking Wins

To get this world-leading result, we employed a neat trick: 鈥榥esting鈥 super efficient quantum error-detecting codes together to make a new, ultra-efficient error-correcting code. Dr. DeCross, a primary author on the paper, said this nesting is like 鈥渂raiding together ropes made out of ropes made out of ropes鈥. 聽Physicists call this 鈥榗ode concatenation鈥, and you can think of it as adding layers of protection on top of each other.

To begin, we took the now-famous 鈥iceberg code鈥, a quantum error detection code that gives an almost 1:1 ratio of physical qubits to logical qubits. The iceberg code only detects errors, however, which means that instead of actually correcting errors it lets you throw out bits where errors were detected. To make a code that could both detect and correct errors, we concatenated two iceberg codes together, giving a code that can correct small errors while still boasting a world-record 2:1 physical:logical ratio (physicists call this a 鈥渉igh encoding rate鈥).

The team then benchmarked the logical qubits, checking large system-scale operations and comparing them to their physical counterparts. This introduces a crucial hurdle to clear: oftentimes, researchers end up with logical qubits that perform *worse* than their physical counterparts. It鈥檚 critical that logical qubits actually beat physical ones, after all 鈥 that is the whole point!

Thanks to some clever circuit design and our natively high fidelities, the new logical qubits outperformed their physical counterparts in every test we performed, sometimes by a factor of 10 to 100.

Computing Logically

Of course, the whole point is to use our logical qubits for something useful, the ultimate measure of functionality. With 64 error-detected qubits, we performed a simulation of quantum magnetism; a crucial milestone that validates our roadmap.

The team took extra care to perform their simulation in 3 dimensions to best reflect the real-world (often, studies like this will only be in 1D or 2D to make them easier). Problems like this are both incredibly important for expanding our understanding of materials, but are also incredibly hard, as their complexity scales quickly. To make qubits interact as if they are in a 3D material when they are trapped in 2D inside the computer, we used our all-to-all connectivity, a feature that results from our movable qubits.

Maximizing Entanglement

Breaking the encoding rate record and performing a world-leading logical simulation wasn鈥檛 enough for the team. For their final feat, the team generated 94 error-detected logical qubits, and entangled them all in a special state called a 鈥淕HZ鈥 state (also known as a 鈥榗at鈥 state, alluding to Schr枚dinger鈥檚 cat). GHZ states are often used by experts as a simple benchmark for showcasing quantum computing鈥檚 unique capacity to use entanglement across many qubits. Our best 94-logical qubit GHZ state boasted a fidelity of 94.9%, crushing its un-encoded counterpart.

Logical Qubits Are the New Normal

Taken together, these results show that we can suppress errors more effectively than ever before, proving that 豆包成人版 is capable of delivering complex, high-fidelity operations that were previously thought to be years away. While the magnetism simulation was only error-detected, it showcases our ability to protect universal computations with partially fault-tolerant methods. On top of that, the team also demonstrated key error-corrected primitives on 豆包成人版 at scale.

All of this has real-world implications for the quantum ecosystem: we are working to package these iceberg codes into QCorrect, an upcoming tool that will help developers automatically improve the performance of their own applications.

This is just the beginning: we are officially entering the era of large-scale logical computing. The path to fault-tolerance is no longer just theoretical鈥攊t is being built, gate by gate, on 豆包成人版.

About 豆包成人版

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

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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鈥檚 largest approximate Quantum Fourier Transforms (QFT) on 豆包成人版 豆包成人版, 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 豆包成人版鈥檚 accuracy and flexible architecture.

While there is ongoing debate around the pace of quantum computing鈥檚 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鈥攆ocusing 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 豆包成人版 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鈥檚 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鈥檚 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 豆包成人版, not only are they crucial for industrial applications but they can also simplify algorithms, making them possible to run now instead of later. This 鈥榯ransformational鈥 approach extends beyond the QFT - other transforms exist, and we have even invented our own quantum-native transforms.

Using our 豆包成人版 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. 聽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 豆包成人版鈥 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鈥檚 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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partnership
July 29, 2026
豆包成人版 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, 豆包成人版, 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鈥攃apabilities 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 豆包成人版鈥檚 豆包成人版 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 豆包成人版鈥檚 豆包成人版 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 (豆包成人版), 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 豆包成人版鈥檚 enterprise partner network.

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July 21, 2026
豆包成人版 SG Grand Challenge 2026

豆包成人版 is pleased to announce that applications are now open for the 豆包成人版 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 豆包成人版 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 豆包成人版 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 豆包成人版 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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