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Quantum computing companies are poised to exceed $1 billion in revenues by the close of 2025, to McKinsey & Company, underscoring how today’s quantum computers are already delivering customer value in their current phase of development.

This figure is projected to reach upwards of $37 billion by 2030, rising in parallel with escalating demand, as well as with the scale of the machines and the complexity of problem sets of which they will be able to address.  

Several systems on the market today are fault-tolerant by design, meaning they are capable of suppressing error-causing noise to yield reliable calculations. However, the full potential of quantum computing to tackle problems of true industrial relevance, in areas like medicine, energy, and finance, remains contingent on an architecture that supports a fully fault-tolerant universal gate set with repeatable error correction—a capability that, until now, has eluded the industry.  

ҹɫֱ�� is the first—and only—company to achieve this critical technical breakthrough, universally recognized as the essential precursor to scalable, industrial-scale quantum computing. This milestone provides us with the most de-risked development roadmap in the industry and positions us to fulfill our promise to deliver our universal, fully fault-tolerant quantum computer, Apollo, by 2029.

In this regard, ҹɫֱ�� is the first company to step from the so-called “NISQ” (noisy intermediate-scale quantum) era towards utility-scale quantum computers.

Unpacking our achievement: first, a ‘full’ primer

A quantum computer uses operations called gates to process information in ways that even today’s fastest supercomputers cannot. The industry typically refers to two types of gates for quantum computers:

• Clifford gates, which can be easily simulated by classical computers, and are relatively easy to implement; and
• Non-Clifford gates, which are usually harder to implement, but are required to enable true quantum computation (when combined with their siblings).

A system that can run both gates is classified as and has the machinery to tackle the widest range of problems. Without non-Clifford gates, a quantum computer is non-universal and restricted to smaller, easier sets of tasks - and it can always be simulated by classical computers. This is like painting with a full palette of primary colors, versus only having one or two to work with. Simply put, a quantum computer that cannot implement ‘non-Clifford’ gates is not really a quantum computer.

A fault-tolerant, or error-corrected, quantum computer detects and corrects its own errors (or faults) to produce reliable results. ҹɫֱ�� has the best and brightest scientists dedicated to keeping our systems’ error rates the lowest in the world.

For a quantum computer to be fully fault-tolerant, every operation must be error-resilient, across Clifford gates and non-Clifford gates, and thus, performing “a full gate set” with error correction. While some groups have performed fully fault-tolerant gate sets in academic settings, these demonstrations were done with only a few qubits and —too high for any practical use.

Today, we have published that establishes ҹɫֱ�� as the first company to develop a complete solution for a universal fully fault-tolerant quantum computer with repeatable error correction, and error rates low enough for real-world applications.

This is where the magic happens

The describes how scientists at ҹɫֱ�� used our System Model H1-1 to perfect magic state production, a crucial technique for achieving a fully fault-tolerant universal gate set. In doing so, they set a record magic state infidelity (7x10^-5), 10x better than any .

Our simulations show that our system could reach a magic state infidelity of 10^-10, or about one error per 10 billion operations, on a larger-scale computer with our current physical error rate. We anticipate reaching 10^-14, or about one error per 100 trillion operations, as we continue to advance our hardware. This means that our roadmap is now derisked.

Setting a record magic state infidelity was just the beginning. The paper also presents the first break-even two-qubit non-Clifford gate, demonstrating a logical error rate below the physical one. In doing so, the team set another record for two-qubit non-Clifford gate infidelity (2x10^-4, almost 10x better than our physical error rate). Putting everything together, the team ran the first circuit that used a fully fault-tolerant universal gate set, a critical moment for our industry.

Flipping the switch

In the , co-authored with researchers at the University of California at Davis, we demonstrated an important technique for universal fault-tolerance called “code switching”.

Code switching describes switching between different error correcting codes. The team then used the technique to demonstrate the key ingredients for universal computation, this time using a code where we’ve previously demonstrated full error correction and the other ingredients for universality.

In the process, the team set a new record for magic states in a distance-3 error correcting code, over 10x better than with error correction. Notably, this process only cost 28 qubits . This completes, for the first time, the ingredient list for a universal gate setin a system that also has real-time and repeatable QEC.

Fully equipped for fault-tolerance

Innovations like those described in these two papers can reduce estimates for qubit requirements by an order of magnitude, or more, bringing powerful quantum applications within reach far sooner.

With all of the required pieces now, finally, in place, we are ‘fully’ equipped to become the first company to perform universal fully fault-tolerant computing—just in time for the arrival of Helios, our next generation system launching this year, and what is very likely to remain as the most powerful quantum computer on the market until the launch of its successor, Sol, arriving in 2027.

If we are to create ‘next-gen’ AI that takes full advantage of the power of quantum computers, we need to start with quantum native transformers. Today we announce yet again that ҹɫֱ�� continues to lead by demonstrating concrete progress — advancing from theoretical models to real quantum deployment.

The future of AI won't be built on yesterday’s tech. If we're serious about creating next-generation AI that unlocks the full promise of quantum computing, then we must build quantum-native models—designed for quantum, from the ground up.

Around this time last year, we introduced Quixer, a state-of-the-art quantum-native transformer. Today, we’re thrilled to announce a major milestone: one year on, Quixer is now running natively on quantum hardware.

Why this matters: Quantum AI, born native

This marks a turning point for the industry: realizing quantum-native AI opens a world of possibilities.

Classical transformers revolutionized AI. They power everything from ChatGPT to real-time translation, computer vision, drug discovery, and algorithmic trading. Now, Quixer sets the stage for a similar leap — but for quantum-native computation. Because quantum computers differ fundamentally from classical computers, we expect a whole new host of valuable applications to emerge.  

Achieving that future requires models that are efficient, scalable, and actually run on today’s quantum hardware.

That’s what we’ve built.

What makes Quixer different?

Until Quixer, quantum transformers were the result of a brute force “copy-paste” approach: taking the math from a classical model and putting it onto a quantum circuit. However, this approach does not account for the considerable differences between quantum and classical architectures, leading to substantial resource requirements.

Quixer is different: it’s not a translation – it's an innovation.

With Quixer, our team introduced an explicitly quantum transformer, built from the ground up using quantum algorithmic primitives. Because Quixer is tailored for quantum circuits, it's more resource efficient than most competing approaches.

As quantum computing advances toward fault tolerance, Quixer is built to scale with it.

What’s next for Quixer?

We’ve already deployed Quixer on real-world data: genomic sequence analysis, a high-impact classification task in biotech. We're happy to report that its performance is already approaching that of classical models, even in this first implementation.

This is just the beginning.

Looking ahead, we’ll explore using Quixer anywhere classical transformers have proven to be useful; such as language modeling, image classification, quantum chemistry, and beyond. More excitingly, we expect use cases to emerge that are quantum-specific, impossible on classical hardware.

This milestone isn’t just about one model. It’s a signal that the quantum AI era has begun, and that ҹɫֱ�� is leading the charge with real results, not empty hype.

Stay tuned. The revolution is only getting started.

Our team is participating in (ISC 2025) from June 10-13 in Hamburg, Germany!

As quantum computing accelerates, so does the urgency to integrate its capabilities into today’s high-performance computing (HPC) and AI environments. At ISC 2025, meet the ҹɫֱ�� team to learn how the highest performing quantum systems on the market, combined with advanced software and powerful collaborations, are helping organizations take the next step in their compute strategy.

ҹɫֱ�� is leading the industry across every major vector: performance, hybrid integration, scientific innovation, global collaboration and ease of access.

• Our industry-leading quantum computer holds the record for performance with a Quantum Volume of 2²³ = 8,388,608 and the highest fidelity on a commercially available QPU available to our users every time they access our systems.
• Our systems have been validated by a #1 ranking against competitors in a recent benchmarking study by Jülich Research Centre.
• We’ve laid out a clear roadmap to reach universal, fully fault-tolerant quantum computing by the end of the decade and will launch our next-generation system, Helios, later this year.
• We are advancing real-world hybrid compute with partners such as RIKEN, NVIDIA, SoftBank, STFC Hartree Center and are pioneering applications such as our own GenQAI framework.

Exhibit Hall

From June 10–13, in Hamburg, Germany, visit us at Booth B40 in the Exhibition Hall or attend one of our technical talks to explore how our quantum technologies are pushing the boundaries of what’s possible across HPC.

Presentations & Demos

Throughout ISC, our team will present on the most important topics in HPC and quantum computing integration—from near-term hybrid use cases to hardware innovations and future roadmaps.

Multicore World Networking Event

• ‍Monday, June 9 | 7:00pm – 9:00 PM at Hofbräu Wirtshaus Esplanade
  ‍In partnership with Multicore World, join us for a ҹɫֱ��-sponsored Happy Hour to explore the present and future of quantum computing with ҹɫֱ�� CCO, Dr. Nash Palaniswamy, and network with our team.

H1 x CUDA-Q Demonstration

• ‍All Week at Booth B40
  ‍We’re showcasing a live demonstration of NVIDIA’s CUDA-Q platform running on ҹɫֱ��’s industry-leading quantum hardware. This new integration paves the way for hybrid compute solutions in optimization, AI, and chemistry.
  ‍Register for a demo

HPC Solutions Forum

• ‍Wednesday, June 11 | 2:20 – 2:40 PM
  ‍“Enabling Scientific Discovery with Generative Quantum AI” – Presented by Maud Einhorn, Technical Account Executive at ҹɫֱ��, discover how hybrid quantum-classical workflows are powering novel use cases in scientific discovery.

See You There!

Whether you're exploring hybrid solutions today or planning for large-scale quantum deployment tomorrow, ISC 2025 is the place to begin the conversation.

We look forward to seeing you in Hamburg!

ҹɫֱ�� has once again raised the bar—setting a record in teleportation, and advancing our leadership in the race toward universal fault-tolerant quantum computing.

Last year, we demonstrating the first-ever fault-tolerant teleportation of a logical qubit. At the time, we outlined how crucial teleportation is to realize large-scale fault tolerant quantum computers. Given the high degree of system performance and capabilities required to run the protocol (e.g., multiple qubits, high-fidelity state-preparation, entangling operations, mid-circuit measurement, etc.), teleportation is recognized as an excellent measure of system maturity.

Today we’re building on last year’s breakthrough, having recently achieved a record logical teleportation fidelity of 99.82% – up from 97.5% in last year’s result. What’s more, our logical qubit teleportation fidelity now exceeds our physical qubit teleportation fidelity, passing the break-even point that establishes our H2 system as the gold standard for complex quantum operations.

This progress reflects the strength and flexibility of our Quantum Charge Coupled Device (QCCD) architecture. The native high fidelity of our QCCD architecture enables us to perform highly complex demonstrations like this that nobody else has yet to match. Further, our ability to perform conditional logic and real-time decoding was crucial for implementing the Steane error correction code used in this work, and our all-to-all connectivity was essential for performing the high-fidelity transversal gates that drove the protocol.

Teleportation schemes like this allow us to “trade space for time,” meaning that we can do quantum error correction more quickly, reducing our time to solution. Additionally, teleportation enables long-range communication during logical computation, which translates to higher connectivity in logical algorithms, improving computational power.

This demonstration underscores our ongoing commitment to reducing logical error rates, which is critical for realizing the promise of quantum computing. ҹɫֱ�� continues to lead in quantum hardware performance, algorithms, and error correction—and we’ll extend our leadership come the launch of our next generation system, Helios, in just a matter of months.

By Bob Coecke and Dimitri Kartsaklis

Introduction

Today we announce the next generation of λambeq , ҹɫֱ��’s quantum natural language processing (QNLP) package.

Incorporating recent developments in both quantum NLP and quantum hardware, λambeq Gen II allows users not only to model the semantics of natural language (in terms of vectors and tensors), but to convert linguistic structures and meaning directly into quantum circuits for real quantum hardware.

Five years ago, our team of Quantum Natural Language Processing (QNLP). In their work, the team realized that there is a direct correspondence between the meanings of words and quantum states, and between grammatical structures and quantum entanglement. As that article put it: “Language is effectively quantum native”.

Our team realized an NLP task on quantum hardware and provided the data and code via , attracting the interest of a then-nascent quantum NLP community, which has since grown around successive releases of λambeq. supported by on the arXiv.

�첹�������: an open-source python library that turns sentences into quantum circuits, and then feeds these to quantum computers subject to VQC methodologies. Initial release in October 2021

From that moment onwards, anyone could play around with QNLP on the then freely available quantum hardware. Our λambeq software has been downloaded over 50,000 times, and the user community is supported by an active , where practitioners can interact with each other and with our development team.  

The QNLP Back-Story

In order to demonstrate that QNLP was possible, even on the hardware available in 2021, we focused exclusively on small noisy quantum computers. Our motivation was to produce some exploratory findings, looking for a potential quantum advantage for natural language processing using quantum hardware. We published in 2016, detailing a quadratic speedup over classical computers (in certain circumstances). We are strongly convinced that there is a lot more potential than indicated in that paper.

That first realization of QNLP marked a shift away from brute-force machine learning, which has now taken the world by storm in the shape of large language models (LLMs) running on algorithms called “transformers”.

Instead of the transformer approach, we decoded linguistic structure using a compositional theory of meaning. With deep roots in computational linguistics, our approach was inspired by research into compositional linguistic algorithms, and such as quantum teleportation. As we continued our work, it became clear that our approach reduced training requirements by relying on a natural relationship between linguistic structure and quantum structure, in practice.

Embedding recent progress in λambeq Gen II

We haven’t sat still, and neither have the teams working in the field of quantum hardware. ҹɫֱ��’s stack now performs at a level we only dreamed of in 2020. While we look forward to continued progress on the hardware front, we are getting ahead of these future developments by shifting the focus in our algorithms and software packages, to ensure we and λambeq’s users are ready to chase far more ambitious goals!

We moved away from the compositional theory of meaning that was the focus of , called DisCoCat, to called DisCoCirc. This enabled us to between text generation and text circuits, concluding that “text circuits are generative for text”.

Formally speaking, DisCoCirc embraces substantially more compositional structure present in language than DisCoCat does, and that pays off in many ways:

• Firstly, the new theoretical backbone enables one to compose the structure of sentences into text structure, so we can now deal with large texts.
• Secondly, the compositional structure of language is represented in a compressed manner, that, in fact, makes the formalism language-neutral, as reported in .
• Thirdly, the augmented compositional linguistic structure, together with the requirement of learnability, makes a quantum model now canonical, and we now have solid theoretical evidence for genuine enhanced performance on quantum hardware, as shown in .  
• Fourthly, the problems associated with trainability of quantum machine learning models vanish, thanks to compositional generalization, which was the .
• Lastly, and surely not least, we of compositional interpretability and explored the myriad ways that it supports explainable AI (XAI), which we also discussed extensively in .

Today, our users can benefit from these recent developments with the release λambeq Gen II. Our open-source tools have always benefited from the attention and feedback we receive from our users. Please give it a try, and we look forward to hearing your feedback on λambeq Gen II.

Enjoy!

We're announcing the world’s first scalable, error-corrected, end-to-end computational chemistry workflow. With this, we are entering the future of computational chemistry.

Quantum computers are uniquely equipped to perform the complex computations that describe chemical reactions – computations that are so complex they are impossible even with the world’s most powerful supercomputers.

However, realizing this potential is a herculean task: one must first build a large-scale, universal, fully fault-tolerant quantum computer – something nobody in our industry has done yet. We are the farthest along that path, as our roadmap, and our robust body of research, proves. At the moment, we have the world’s most powerful quantum processors, and are moving quickly towards universal fault tolerance. Our commitment to building the best quantum computers is proven again and again in our world-leading results.

While we do the work to build the world’s best quantum computers, we aren’t waiting to develop their applications. We have teams working right now on making sure that we hit the ground running with each new hardware generation. In fact, our team has just taken a huge leap forward for computational chemistry using our System Model H2.

In our latest paper, we have announced the first-ever demonstration of a scalable, end-to-end workflow for simulating chemical systems with quantum error correction (QEC). This milestone shows that quantum computing will play an essential role, in tandem with HPC and AI, in unlocking new frontiers in scientific discovery.

In the paper, we showcase the first practical combination of quantum phase estimation (QPE) with logical qubits for molecular energy calculations – an essential step toward fault-tolerant quantum simulations. It builds on our and marks a critical step toward achieving quantum advantage in chemistry.  

By demonstrating this end-to-end workflow on our H2 quantum computer using our state-of-the-art chemistry platform InQuanto™, we are proving that quantum error-corrected chemistry simulations are not only feasible, but also scalable and —crucially—implementable in our quantum computing stack.

This work sets key benchmarks on the path to fully fault-tolerant quantum simulations. Building such capabilities into an industrial workflow will be a milestone for quantum computing, and the demonstration reported here represents a new high-water mark as we continue to lead the global industry in pushing towards universal fault-tolerant computers capable of widespread scientific and commercial advantage.  

As we look ahead, this workflow will serve as the foundation for future quantum-HPC integration, enabling chemistry simulations that are impossible today.

Showcasing ҹɫֱ��’s Full-Stack Advantage

Today’s achievement wouldn’t be possible without the ҹɫֱ��’s full stack approach. Our vertical integration - from hardware to software to applications - ensures that each layer works together seamlessly.  

Our H2 quantum computer, based on the scalable QCCD architecture with its unique combination of high-fidelity operations, all-to-all connectivity, mid-circuit measurements and conditional logic, enabled us to run more complex quantum computing simulations than previously possible. The work also leverages ҹɫֱ��’s real-time QEC decoding capability and benefitted from the quantum error correction advantages also provided by QCCD.

We will make this workflow available to customers via InQuanto, our quantum chemistry platform, allowing users to easily replicate and build upon this work. The integration of high-quality quantum computing hardware with sophisticated software creates a robust environment for iterating and accelerating breakthroughs in fields like chemistry and materials science.

A Collaborative Future: The Role of AI and Supercomputing

Achieving quantum advantage in chemistry will require more than just quantum hardware; it will require a synergistic approach that combines such quantum computing workflows demonstrated here with classical supercomputing and AI. Our strategic partnerships with leading supercomputing providers – with ҹɫֱ�� being selected as a founding collaborator for NVIDIA’s Accelerated Quantum Research Center – as well as our commitment to exploring generative quantum AI, place us in a unique position to maximize the benefit of quantum computing, and supercharge quantum advantage with the integration of classical supercomputing and AI.

Conclusion

Quantum computing holds immense potential for transforming industries across the globe. Our work today experimentally demonstrates the first complete and scalable quantum chemistry simulation, showing that the long-awaited quantum advantage in simulating chemical systems is not only possible, but within reach. With the development of new error correction techniques and the continued advancement of our quantum hardware and software we are paving the way for a future where quantum simulations can address challenges that are impossible today. ҹɫֱ��’s ongoing collaborations with HPC providers and its exploration of AI-driven quantum techniques position our company to capitalize on this trifecta of computing power and achieve meaningful breakthroughs in quantum chemistry and beyond.

We encourage you to explore this breakthrough further by reading and for yourself.