11 citations · 27 across the 11 of their papers we have counts for
11 papers
Block entropy area based non-local fermionic mode optimization with gradient disentanglers
Miklós Antal Werner, Gero Friesecke, Andor Menczer +2
We introduce a systematic block entropy area based mode optimization algorithm for many-body quantum states of interacting fermions represented by matrix product states. From the g…
Hunting for quantum advantage in electronic structure calculations is a highly non-trivial task
Örs Legeza, Andor Menczer, Miklós Antal Werner +7
In light of major developments over the past decades in both quantum computing and simulations on classical hardware, it is a serious challenge to identify a real-world problem whe…
Mixed-precision ab initio tensor network state methods adapted for NVIDIA Blackwell technology via emulated FP64 arithmetic
Cole Brower, Samuel Rodriguez Bernabeu, Jeff Hammond +5
We report cutting-edge performance results via mixed-precision spin adapted ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations utilizing the Oz…
Orbital optimization of large active spaces via AI-accelerators
Örs Legeza, Andor Menczer, Ádám Ganyecz +6
We present an efficient orbital optimization procedure that combines the highly GPU accelerated, spin-adapted density matrix renormalization group (DMRG) method with the complete a…
Tensor network state methods and quantum information theory for strongly correlated molecular systems
Miklós Antal Werner, Andor Menczer, Örs Legeza
A brief pedagogical overview of recent advances in tensor network state methods are presented that have the potential to broaden their scope of application radically for strongly c…
Cost optimized ab initio tensor network state methods: industrial perspectives
Andor Menczer, Örs Legeza
We introduce efficient solutions to optimize the cost of tree-like tensor network state method calculations when an expensive GPU-accelerated hardware is utilized. By supporting a…
Parallel implementation of the Density Matrix Renormalization Group method achieving a quarter petaFLOPS performance on a single DGX-H100 GPU node
Andor Menczer, Maarten van Damme, Alan Rask +5
We report cutting edge performance results for a hybrid CPU-multi GPU implementation of the spin adapted ab initio Density Matrix Renormalization Group (DMRG) method on current sta…
Global fermionic mode optimization via swap gates
Gero Friesecke, Miklós Antal Werner, Kornél Kapás +2
An optimal choice of single-particle modes leads to an optimal tensor network-based compression of the many-body wave function for systems of indistinguishable particles, which com…
Two dimensional quantum lattice models via mode optimized hybrid CPU-GPU density matrix renormalization group method
Andor Menczer, Kornél Kapás, Miklós Antal Werner +1
We present a hybrid numerical approach to simulate quantum many body problems on two spatial dimensional quantum lattice models via the non-Abelian ab initio version of the density…
Boosting the effective performance of massively parallel tensor network state algorithms on hybrid CPU-GPU based architectures via non-Abelian symmetries
Andor Menczer, Örs Legeza
We present novel algorithmic solutions together with implementation details utilizing non-Abelian symmetries in order to boost the current limits of tensor network state algorithms…
Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures
Andor Menczer, Örs Legeza
The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for…