446 citations
- Lawrence Livermore National LaboratoryUS25 papers
- University of KentuckyUS25 papers
- Los Alamos National LaboratoryUS21 papers
- Royal Observatory of BelgiumBE20 papers
- University of OxfordGB18 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR14 papers
- Rutherford Appleton LaboratoryGB14 papers
- University of RochesterUS14 papers
- Imperial College LondonGB13 papers
- University of StrathclydeGB13 papers
- Centre National de la Recherche ScientifiqueFR12 papers
- Queen's University BelfastGB12 papers
5 papers · 1 filter
Deep Circuit Compression for Quantum Dynamics via Tensor Networks
Joe Gibbs, Lukasz Cincio
Dynamic quantum simulation is a leading application for achieving quantum advantage. However, high circuit depths remain a limiting factor on near-term quantum hardware. We present…
High-fidelity dimer excitations using quantum hardware
Norhan M. Eassa, Joe Gibbs, Zoe Holmes +7
Many-body entangled quantum spin systems exhibit emergent phenomena such as topological quantum spin liquids with distinct excitation spectra accessed in inelastic neutron scatteri…
The power and limitations of learning quantum dynamics incoherently
Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph +4
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share qu…
Dynamical simulation via quantum machine learning with provable generalization
Joe Gibbs, Zoë Holmes, Matthias C. Caro +5
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to e…
Out-of-distribution generalization for learning quantum dynamics
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell +5
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution gen…