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λambeq Gen II: A Quantum-Enhanced Interpretable and Scalable Text-based NLP Software Package

May 22, 2025
By Bob Coecke and Dimitri Kartsaklis
Introduction

Today we announce the next generation of λambeq , ˰’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. ˰’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!

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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partnership
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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events
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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