Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.
Ab Initio Quantum Chemistry using the Density Matrix Renormalization Group
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abstract
In this paper we describe how the density matrix renormalization group (DMRG) can be used for quantum chemical calculations for molecules, as an alternative to traditional methods, such as configuration interaction or coupled cluster approaches. As a demonstration of the potential of this approach, we present results for the H$_2$O molecule in a standard gaussian basis. Results for the total energy of the system compare favorably with the best traditional quantum chemical methods.
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UNVERDICTED 2representative citing papers
TensorFlow-backed TensorNetwork implementation of MERA for critical 1D Ising model with conformal data extraction and 200x GPU acceleration reported.
citing papers explorer
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Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm
Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.
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TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
TensorFlow-backed TensorNetwork implementation of MERA for critical 1D Ising model with conformal data extraction and 200x GPU acceleration reported.