9 papers
Hybrid Quantum-Classical Neural Networks for Recognizing Quantum Phases
Colin Scarato, Johannes Knörzer, Markus K. Hoffmann +11
Identifying quantum phases of matter is key to understanding strongly correlated materials, but remains a challenging task for both conventional computers and current quantum proce…
Hybrid quantum-classical neural network for sample-efficient recognition of topological phases
Markus K. Hoffmann, Leon C. Sander, Colin Scarato +4
With increasing maturity of quantum computers, standard methods for characterizing global properties of their output quantum states via direct measurements and classical post-proce…
Thermodynamic-limit dispersion relations on trapped-ion quantum hardware
Lucas Marti, Sumeet, Stefan Wolf +2
We run a numerical linked-cluster expansion with a quantum algorithm (NLCE+QA), computing ground-state energies and one quasi-particle dispersions in the thermodynamic limit using…
Shot-noise reduction for lattice Hamiltonians
Timo Eckstein, Refik Mansuroglu, Stefan Wolf +4
Efficiently estimating energy expectation values of quantum lattice systems on quantum computers is a crucial subroutine for various quantum algorithms, which can lead to significa…
Sample-Based Quantum Diagonalization with Amplitude Amplification
Nina Stockinger, Ludwig Nützel, Michael J. Hartmann
Recently, sample-based quantum diagonalization (SQD) has emerged as a promising approach to compute ground and excited states of problem Hamiltonians.This method classically diagon…
Variational Time Evolution Compression for Solving Impurity Models on Quantum Hardware
Stefan Wolf, Martin Eckstein, Michael J. Hartmann
Dynamical mean-field theory (DMFT) is a useful tool to analyze models of strongly correlated fermions like the Hubbard model. In DMFT, the lattice of the model is replaced by a sin…