11 citations · 25 across the 11 of their papers we have counts for
11 papers · 1 filter
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…
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…
Ground State Energy via Adiabatic Evolution and Phase Measurement for a Molecular Hamiltonian on an Ion-Trap Quantum Computer
Ludwig Nützel, Michael J. Hartmann, Henrik Dreyer +1
Estimating molecular ground-state energies is a central application of quantum computing, requiring both the preparation of accurate quantum states and efficient energy readout. Un…
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…
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…
Solving an Industrially Relevant Quantum Chemistry Problem on Quantum Hardware
Ludwig Nützel, Alexander Gresch, Lukas Hehn +10
Quantum chemical calculations are among the most promising applications for quantum computing. Implementations of dedicated quantum algorithms on available quantum hardware were so…
Quantum Convolutional Neural Network for Phase Recognition in Two Dimensions
Leon C. Sander, Nathan A. McMahon, Petr Zapletal +1
Quantum convolutional neural networks (QCNNs) are quantum circuits for characterizing complex quantum states. They have been proposed for recognizing quantum phases of matter at lo…
Efficient Quantum Cooling Algorithm for Fermionic Systems
Lucas Marti, Refik Mansuroglu, Michael J. Hartmann
We present a cooling algorithm for ground state preparation of fermionic Hamiltonians. Our algorithm makes use of the Hamiltonian simulation of the considered system coupled to an…
Quantum Tensor Product Decomposition from Choi State Tomography
Refik Mansuroglu, Arsalan Adil, Michael J. Hartmann +2
The Schmidt decomposition is the go-to tool for measuring bipartite entanglement of pure quantum states. Similarly, it is possible to study the entangling features of a quantum ope…