6 papers
Quantum machine learning models for graphs
Frédéric Sauvage, Pranav Kalidindi, Frederic Rapp +1
Geometric Machine Learning (GML) successes have been achieved through the thorough study and design of new equivariant neural networks. In comparison, geometric quantum machine lea…
Unbiased Hamiltonian Simulation by Reversing Trotter Error Dynamics
Keisuke Murota, Yuta Kikuchi, Enrico Rinaldi +3
Owing to their simplicity and low overhead, Suzuki-Trotter formulas remain the de facto Hamiltonian simulation methods on current quantum computing platforms. Systematic Trotter er…
Exact log-depth preparation of highly entangled matrix product states
Keisuke Murota, Frédéric Sauvage, Marco Ballarin +2
Preparing matrix product states (MPS) on a quantum device is a key subroutine in many quantum algorithms. The most competitive methods, based on the renormalisation group, prepare…
Classical shadows with arbitrary group representations
Maxwell West, Frederic Sauvage, Aniruddha Sen +6
Classical shadows (CS) has recently emerged as an important framework to efficiently predict properties of an unknown quantum state. A common strategy in CS protocols is to paramet…
More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing
Aroosa Ijaz, C. Huerta Alderete, Frédéric Sauvage +3
Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a c…
Lie-algebraic classical simulations for quantum computing
Matthew L. Goh, Martin Larocca, Lukasz Cincio +2
The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity, and in the development of quantum technologies. Efficient technique…