Introducing LAMBEQ
A Toolkit for Quantum Natural Language Processing
The new software development toolkit for quantum natural language processing tested and benchmarked on System Model H1 technology
Telling Alexa to play “Schrodinger’s Cat” by Tears for Fears. Asking Siri for directions to a quantum-themed bar or restaurant. A smart phone autocorrecting a word in a text message.
These are everyday applications of natural language processing – NLP for short – a field of artificial intelligence that focuses on training computers to understand words and conversations with the same reasoning as humans.
NLP technologies have advanced rapidly in recent years with the help of increasingly powerful computing clusters that can run language models that examine reams of text and count how often certain words appear. These models train devices to retrieve information, annotate text, translate words from one language to another, answer questions, and perform other tasks.
The next step is to “teach” computers to infer meaning, understand nuance, and grasp the context of conversations. To do that, however, requires massive computational resources and multiple algorithms or data structures.
A United Kingdom-based quantum computing company believes the answer lies with qubits, superposition, and entanglement.
Cambridge Quantum recently released lambeq, a new open-source software development toolkit, that enables researchers to convert sentences into quantum circuits that can be run on quantum computers. It is the first toolkit developed specifically for quantum natural language processing – or QNLP - and was tested on System Model H1 technology before it was released.
The software takes the text, parses it, and then uses linguistics and mathematics to differentiate between a verb, noun, preposition, adjectives, etc., and label them to understand the relationships between words.
Cambridge Quantum researchers tested 30 sentences on the System Model H1, which was able to classify words correctly 87 percent of the time.
“We deem that a success,” said Konstantinos Meichannetzidis, a member of the CQ team. “We found that our software works well with the Honeywell technology and were able to benchmark the performance of this quantum device.”
The lambeq project also represented a first for Honeywell Quantum Solutions. It was the first QNLP problem run on the System Model H1 hardware.
“We are really excited to be a part of this work and contribute to the development of this important toolkit,” said Tony Uttley, president of Honeywell Quantum Solutions. “Applications like this help us test our system and understand how well it performs solving different problems.”
(Honeywell Quantum Solutions and Cambridge Quantum have a long-standing history of partnering together on research and other projects that benefit end-customers. The two entities announced in June they are seeking regulatory approval to combine to form a new company.)
Why QNLP?
For humans, decoding conversations to understand meaning is a complex process. We infer meaning through tone of voice, body language, context, location, and other factors. For computers, which do not rely on heuristics, decoding language is even more complex.
The only way to create some sort of “meaning-aware” NLP is to explicitly encode compositional, semantic sentence structure into language models. To do this on a classical computer, however, requires massive computational resources, which are costly, and would likely still take months to process.
Quantum computers, on the other hand, run calculations and crunch data very differently.
They harness unique properties of quantum physics, specifically superposition and entanglement, to store and process information. Because of that, these systems can examine problems with multiple states and evaluate a large space of possible answers simultaneously.
What this means in terms of natural language processing is that quantum computers are likely to go beyond counting how often certain words appear or are used together. As noted above, quantum computers can identify words, label them as a noun, verb, preposition, etc., and understand the relationship between words. (lambeq uses the Distributional Compositional Categorical – or DisCoCat – model to do this.)
This enables the computer to infer meaning, and also provides insight into how and why the computer made connections between words. The latter is important for validating data and also expanding the use of QNLP in regulated sectors such as finance, legal, and medicine where transparency is critical.
Built upon previous work
The Cambridge Quantum team has long explored how quantum computing can advance natural language processing, and has published extensively on the topic.
In December 2020, researchers released two foundational papers that demonstrated that QNLP is inherently meaning-aware and can successfully interpret questions and respond.
Earlier this year, the team performed the first NLP experiment conducted on a quantum computer by converting more than 100 sentences into quantum circuits using an IBM technology. Researchers successfully trained two NLP models to classify words in sentences.
The release of lambeq and the testing of the open-source toolkit on the Honeywell System Model H1 represents the next steps in their QNLP efforts.
“Our team has been involved in foundational work that explores how quantum computers can be used to solve some of the most intractable problems in artificial intelligence,” said Bob Coecke, Cambridge Quantum’s chief scientist.
“In various papers published over the course of the past year,” Coecke added, “We have not only provided details on how quantum computers can enhance NLP but also demonstrated that QNLP is ‘quantum native,’ meaning the compositional structure governing language is mathematically the same as that governing quantum systems. This will ultimately move the world away from the current paradigm of AI that relies on brute force techniques that are opaque and approximate.”
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.
Quantinuum is focusing on redefining what’s possible in hybrid quantum–classical computing by integrating Quantinuum’s best-in-class systems with high-performance NVIDIA accelerated computing to create powerful new architectures that can solve the world’s most pressing challenges.
The launch of Helios, Powered by Honeywell, the world’s most accurate quantum computer, marks a major milestone in quantum computing. Helios is now available to all customers through the cloud or on-premise deployment, launched with a go-to-market offering that seamlessly pairs Helios with the NVIDIA Grace Blackwell platform, targeting specific end markets such as drug discovery, finance, materials science, and advanced AI research.
We are also working with NVIDIA to adopt NVIDIA NVQLink, an open system architecture, as a standard for advancing hybrid quantum-classical supercomputing. Using this technology with Quantinuum Guppy and the NVIDIA CUDA-Q platform, Quantinuum has implemented NVIDIA accelerated computing across Helios and future systems to perform real-time decoding for quantum error correction.
In an industry-first demonstration, an NVIDIA GPU-based decoder integrated in the Helios control engine improved the logical fidelity of quantum operations by more than 3% — a notable gain given Helios’ already exceptionally low error rate. These results demonstrate how integration with NVIDIA accelerated computing through NVQLink can directly enhance the accuracy and scalability of quantum computation.
This unique collaboration spans the full Quantinuum technology stack. Quantinuum’s next-generation software development environment allows users to interleave quantum and GPU-accelerated classical computations in a single workflow. Developers can build hybrid applications using tools such as NVIDIA CUDA-Q, NVIDIA CUDA-QX, and Quantinuum’s Guppy, to make advanced quantum programming accessible to a broad community of innovators.
The collaboration also reaches into applied research through the NVIDIA Accelerated Quantum Computing Research Center (NVAQC), where an NVIDIA GB200 NVL72 supercomputer can be paired with Quantinuum’s Helios to further drive hybrid quantum-GPU research, including the development of breakthrough quantum-enhanced AI applications.
A recent achievement illustrates this potential: The ADAPT-GQE framework, a transformer-based Generative Quantum AI (GenQAI) approach, uses a Generative AI model to efficiently synthesize circuits to prepare the ground state of a chemical system on a quantum computer. Developed by Quantinuum, NVIDIA, and a pharmaceutical industry leader—and leveraging NVIDIA CUDA-Q with GPU-accelerated methods—ADAPT-GQE achieved a 234x speed-up in generating training data for complex molecules. The team used the framework to explore imipramine, a molecule crucial to pharmaceutical development. The transformer was trained on imipramine conformers to synthesize ground state circuits at orders of magnitude faster than ADAPT-VQE, and the circuit produced by the transformer was run on Helios to prepare the ground state using InQuanto, Quantinuum's computational chemistry platform.
From collaborating on hardware and software integrations to GenQAI applications, the collaboration between Quantinuum and NVIDIA is building the bridge between classical and quantum computing and creating a future where AI becomes more expansive through quantum computing, and quantum computing becomes more powerful through AI.
By Dr. Noah Berthusen
The earliest works on quantum error correction showed that by combining many noisy physical qubits into a complex entangled state called a "logical qubit," this state could survive for arbitrarily long times. QEC researchers devote much effort to hunt for codes that function well as "quantum memories," as they are called. Many promising code families have been found, but this is only half of the story.
Being able to keep a qubit around for a long time is one thing, but to realize the theoretical advantages of quantum computing we need to run quantum circuits. And to make sure noise doesn't ruin our computation, these circuits need to be run on the logical qubits of our code. This is often much more challenging than performing gates on the physical qubits of our device, as these "logical gates" often require many physical operations in their implementation. What's more, it often is not immediately obvious which logical gates a code has, and so converting a physical circuit into a logical circuit can be rather difficult.
Some codes, like the famous surface code, are good quantum memories and also have easy logical gates. The drawback is that the ratio of physical qubits to logical qubits (the "encoding rate") is low, and so many physical qubits are required to implement large logical algorithms. High-rate codes that are good quantum memories have also been found, but computing on them is much more difficult. The holy grail of QEC, so to speak, would be a high-rate code that is a good quantum memory and also has easy logical gates. Here, we make progress on that front by developing a new code with those properties.
Building on prior error correcting codes
A recent work from Quantinuum QEC researchers introduced genon codes. The underlying construction method for these codes, called the "symplectic double cover," also provided a way to obtain logical gates that are well suited for Quantinuum's QCCD architecture. Namely, these "SWAP-transversal" gates are performed by applying single qubit operations and relabeling the physical qubits of the device. Thanks to the all-to-all connectivity facilitated through qubit movement on the QCCD architecture, this relabeling can be done in software essentially for free. Combined with extremely high fidelity (~1.2 x10-5) single-qubit operations, the resulting logical gates are similarly high fidelity.
Given the promise of these codes, we take them a step further in our new paper. We combine the symplectic double codes with the [[4,2,2]] Iceberg code using a procedure called "code concatenation". A concatenated code is a bit like nesting dolls, with an outer code containing codes within it---with these too potentially containing codes. More technically, in a concatenated code the logical qubits of one code act as the physical qubits of another code.
The new codes, which we call "concatenated symplectic double codes", were designed in such a way that they have many of these easily-implementable SWAP-transversal gates. Central to its construction, we show how the concatenation method allows us to "upgrade" logical gates in terms of their ease of implementation; this procedure may provide insights for constructing other codes with convenient logical gates. Notably, the SWAP-transversal gate set on this code is so powerful that only two additional operations (logical T and S) are necessary for universal computation. Furthermore, these codes have many logical qubits, and we also present numerical evidence to suggest that they are good quantum memories.
Concatenated symplectic double codes have one of the easiest logical computation schemes, and we didn’t have to sacrifice rate to achieve it. Looking forward in our roadmap, we are targeting hundreds of logical qubits at ~ 1x 10-8 logical error rate by 2029. These codes put us in a prime position to leverage the best characteristics of our hardware and create a device that can achieve real commercial advantage.
Every year, the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) brings together the global supercomputing community to explore the technologies driving the future of computing.
At this year’s conference, from November 16th – 21st in St. Louis, Missouri, Quantinuum showcased how our quantum hardware, software, and partnerships are helping define the next era of high-performance and quantum computing.
Quantinuum in the Expo Hall
The Quantinuum team was on-site at booth #4432 to showcase how we’re building the bridge between HPC and quantum. Folks stopped by our booth to see:
- Live demo unit of our quantum hardware
- Our new Helios replica, providing an up-close look at the design behind our next-generation system
- The Helios chip, highlighting the innovation driving the world’s most advanced trapped-ion quantum computers
Our quantum computing experts hosted daily tutorials at our booth on Helios, our next-generation hardware platform, Nexus, our all-in-one quantum computing platform, and Hybrid Workflows, featuring the integration of NVIDIA CUDA-Q with Quantinuum Systems.
Speaking Sessions at SC25
Join our team as they share insights on the opportunities and challenges of quantum integration within the HPC ecosystem:
Panel Session: The Quantum Era of HPC: Roadmaps, Challenges and Opportunities in Navigating the Integration Frontier
November 19th | 10:30 – 12:00pm CST
During this panel session, Kentaro Yamamoto from Quantinuum, will join experts from Lawrence Berkeley National Laboratory, IBM, QuEra, RIKEN, and Pawsey Supercomputing Research Centre to explore how quantum and classical systems are being brought together to accelerate scientific discovery and industrial innovation.
BoF Session: Bridging the Gap: Making Quantum-Classical Hybridization Work in HPC
November 19th | 5:15 – 6:45pm CST
Quantum-classical hybrid computing is moving from theory to reality, yet no clear roadmap exists for how best to integrate quantum processing units (QPUs) into established HPC environments. In this Birds of a Feather discussion, co-led by Quantinuum’s Grahame Vittorini and representatives from BCS, DOE, EPCC, Inria, ORNL NVIDIA, and RIKEN we hope to bring together a global community of HPC practitioners, system architects, quantum computing specialists and workflow researchers, including participants in the Workflow Community Initiative, to assess the state of hybrid integration and identify practical steps toward scalable, impactful deployment.