76 citations · 309 across the 17 of their papers we have counts for
4 papers · 1 filter
Symmetry-invariant quantum machine learning force fields
Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher +2
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate…
Counterdiabatic optimized driving in quantum phase sensitive models
Francesco Pio Barone, Oriel Kiss, Michele Grossi +2
State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols acros…
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa +2
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies…
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the…