1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Hybrid Quantum-Classical Machine Learning with String Diagrams
Alexander Koziell-Pipe, Aleks Kissinger
Central to near-term quantum machine learning is the use of hybrid quantum-classical algorithms. This paper develops a formal framework for describing these algorithms in terms of…
Functorial Language Models
Alexis Toumi, Alex Koziell-Pipe
We introduce functorial language models: a principled way to compute probability distributions over word sequences given a monoidal functor from grammar to meaning. This yields a m…