AI + quantum computing: Quantinuum, NVIDIA, and Pfizer have combined transformer-based generative AI with quantum computing to automatically generate high-quality quantum chemistry circuits more efficiently than traditional optimization methods. Practical pharma impact: The approach was used to prepare molecular ground states and validated on Quantinuum’s Helios hardware, demonstrating a path toward larger-scale computational chemistry and drug discovery. Long-term vision: The team aims to build quantum foundation models that learn from increasingly complex quantum data, eventually enabling AI to design circuits for molecules too large for classical simulation. Quantum computing has long promised a future that expands what we can do with compute — for example, in molecular simulation, materials discovery, or pharmaceuticals development. But between that promise and practical utility sits a stubborn bottleneck: quantum state preparation. To run any algorithm on a quantum computer, you must first put the qubits in the right starting state. Think of it like setting up a Rube Goldberg machine- except in this case, you’re not sure exactly which initial setup will give you the results you want. This is what makes quantum state preparation so important: your choice of initial state dictates the accuracy and cost of the rest of the calculation. We teamed up with NVIDIA and Pfizer to tackle this problem, with an eye towards developing meaningful industrial workflows. The result is a new generative quantum AI framework, called ADAPT-GQE, which we consider to be a canonical instance of GenQAI. ADAPT-GQE uses quantum data to train transformer models that ultimately synthesize quantum chemistry circuits faster, with better outcomes, in a sort of ‘virtuous cycle’. Ultimately, this means we have developed a new interface between quantum computing and AI. By treating quantum circuit generation as a language modelling problem, we now have a system that can generate high-quality ground-state preparation circuits -