4 papers
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
MOLPIPx: an end-to-end differentiable package for permutationally invariant polynomials in Python and Rust
Manuel S. Drehwald, Asma Jamali, Rodrigo A. Vargas-Hernández
In this work, we present MOLPIPx, a versatile library designed to seamlessly integrate Permutationally Invariant Polynomials (PIPs) with modern machine learning frameworks, enablin…
Quantum Deep Equilibrium Models
Philipp Schleich, Marta Skreta, Lasse B. Kristensen +2
The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth o…
GFlowNets for Hamiltonian decomposition in groups of compatible operators
Isaac L. Huidobro-Meezs, Jun Dai, Guillaume Rabusseau +1
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical…