73 citations · 160 across the 9 of their papers we have counts for
10 papers
Quantum simulation of actinide chemistry: towards scalable algorithms on trapped ion quantum computers
Kesha Sorathia, Cono Di Paola, Gabriel Greene-Diniz +9
Due to the wide range of technical applications of actinide elements, a thorough understanding of their electronic structure could complement technological improvements in many dif…
Resource-Efficient Simulations of Particle Scattering on a Digital Quantum Computer
Yahui Chai, Joe Gibbs, Vincent R. Pascuzzi +4
We develop and demonstrate methods for simulating the scattering of particle wave packets in the interacting Thirring model on digital quantum computers, with hardware implementati…
Learning Circuits with Infinite Tensor Networks
Joe Gibbs, Lukasz Cincio
Hamiltonian simulation on quantum computers is strongly constrained by gate counts, motivating techniques to reduce circuit depths. While tensor networks are natural competitors to…
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…
Exploiting symmetries in nuclear Hamiltonians for ground state preparation
Joe Gibbs, Zoë Holmes, Paul Stevenson
The Lipkin and Agassi models are simplified nuclear models that provide natural test beds for quantum simulation methods. Prior work has investigated the suitability of the Variati…
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…
Long-time simulations with high fidelity on quantum hardware
Joe Gibbs, Kaitlin Gili, Zoë Holmes +5
Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while…