Senior Analyst, AI & Quantum Computing, Paul Smith-Goodson, dives in as Quantinuum is an integrated software-hardware quantum computing company that uses trapped-ion for its compute technology. It recently released a significant update to its Lambeq open-source Python library and toolkit.
Current NLP language models built with transformer models and deep neural networks consume considerable energy creating environmental concerns. Within the decade, quantum computers will scale from hundreds to millions of qubits, enabling expanded versions of QNLP that will be more efficient, faster, handle enormous datasets, and require less power with a lower environmental impact.
A new supervised training module simplifies training parameterized quantum circuits and tensor networks for machine learning Since its introduction, researchers have used the toolkit to advance practical, real-world QNLP applications such as automated dialogue, text mining, language translation, text-to-speech, language generation, and bioinformatics.Converting a sentence into quantum circuits is complicated. However, QNLP has the advantage of being ‘quantum native,’ which means language has a similar compositional structure mathematically as the structure used in quantum systems.
After being encoded, tensor networks or quantum circuits implemented with TKET are ready to be optimized for machine learning tasks such as text classification.It is important to note that two out of three true QNLP pioneers now work together as senior researchers at Quantinuum. Professor Stephen Clark is Quantinuum’s Head of Artificial Intelligence, and Professor Bob Coecke is its Chief Scientist.
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