#QuantumNaturalLanguageProcessing

[govindhtech]

λambeq Gen II: QNLP Quantum Natural Language Processing

QNLP Quantum Natural Language Processing

Text-based Natural Language Processing Software, λambeq Gen II, is now quantum-enabled and scalable.

Quantinuum introduces λambeq, their latest quantum natural language processing (QNLP) software.

λambeq Gen II, Quantinuum's latest open-source QNLP program, is a noteworthy development. Since May 22, 2025, λambeq Gen II allows users to convert phrases into quantum circuits that can be operated on quantum hardware. This new version builds on five years of development and incorporates language formalism and quantum hardware advances.

The switch from DisCoCat to DisCoCirc mathematical underpinning is a major change in Gen II. DisCoCirc has more linguistic compositional structure than DisCoCat. This new formalism supports scalability to full texts rather than sentences, enabling more expressive compositionality across languages and broader text structures. DisCoCirc also ensures that the resulting quantum models are canonical, meaning well-defined in compositional structure and learnability, and language neutral, making them versatile across linguistic domains.

These traits help solve QNLP study interpretability and trainability issues. Explainable AI (XAI) features, compositional generalisation, and quantum performance improvements support the release. The open-source λambeq program connects with Quantinuum's quantum stack and swiftly converts syntactic structures into quantum circuits using Python and VQC techniques.

Another notable announcement was the first scaled, error-corrected, end-to-end computational chemical procedure. Quantum computers are suited for complex computations describing chemical phenomena that classical supercomputers cannot handle, opening up new scientific study avenues.

This work showed the first successful coupling of logical qubits and quantum phase estimation (QPE) for molecular energy computations, a step towards fault-tolerant quantum simulations. The demonstration used Quantinuum's System Model H2 quantum computer and InQuanto quantum chemistry platform. Quantinuum's full-stack strategy is shown by the H2 hardware's scalable QCCD design, high-fidelity operations, all-to-all connection, conditional logic, mid-circuit measurements, and real-time QEC decoding. Quantinuum says InQuanto will offer this workflow.

Also announced is a partnership with Al Rabban Capital to accelerate quantum technology commercialisation in Qatar and the Gulf. This partnership allows Qatar to invest $1 billion USD over ten years. This cooperation integrates Quantinuum's cutting-edge quantum technology, co-creates regional quantum computing applications, and grows workforce.

This effort strengthens Quantinuum's desire to increase commercial reach through long-term strategic alliances, demonstrating the US and Qatar's commitment to strategic relations and bilateral investment in future-defining industries. Qatar discussions focused on hybrid quantum-classical systems that combine high-performance computing, AI, and quantum computing to improve AI across industries. Quantinuum's H2-1 supercomputer, which uses less power, may help AI-powered data centres fulfil energy needs.

Additionally, Quantinuum reported that their System Model H2's Quantum Volume (QV) exceeded 2²³ = 8,388,608. They met their 2020 pledge to increase QV by 10 times annually for five years. IBM devised the Quantum Volume benchmark to measure a machine's computational capacity using qubit count, coherence times, connection, and error rates. IBM believes that a larger quantum volume allows for more realistic problem-solving. Quantinuum defines QV as a non-gamable statistic that incorporates fault tolerance factors. Quantinuum's world-record QV proves its outstanding performance. They say they finished this five-year commitment early.

Other recent news includes HBKU's $10 million MoD grant to build Qatar's first quantum computing lab.