3 papers
quant-ph2026
Learning to Prepare Molecular Ground States with Transformer Models
Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit +14
Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistr…
quant-ph2026
Automated near-term quantum algorithm discovery for molecular ground states
Fabian Finger, Frederic Rapp, Pranav Kalidindi +10
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform…
quant-ph2026
Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes
Daniel Mills, Ifan Williams, Jacob Swain +3
Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which…