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Neural-network quantum states for many-body physics
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Variational quantum calculations have borrowed many tools and algorithms from the machine learning community in the recent years. Leveraging great expressive power and efficient gradient-based optimization, researchers have shown that trial states inspired by deep learning problems can accurately model many-body correlated phenomena in spin, fermionic and qubit systems. In this review, we derive the central equations of different flavors variational Monte Carlo (VMC) approaches, including ground state search, time evolution and overlap optimization, and discuss data-driven tasks like quantum state tomography. An emphasis is put on the geometry of the variational manifold as well as bottlenecks in practical implementations. An overview of recent results of first-principles ground-state and real-time calculations is provided.
Forward citations
Cited by 4 Pith papers
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Hard-coding translational, reflection, and bit-flip symmetries into Boltzmann-style neural quantum states cuts parameters from thousands to tens and speeds up training while preserving ground-state accuracy, with new ...
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Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations
The electron-hole kernel can be approximated as a small diagonal part plus a low-rank off-diagonal part via SVD, enabling at least a 10x speedup of TD-aGW calculations with modest loss of accuracy.
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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications
A variational Monte Carlo algorithm that trains a restricted Boltzmann machine quantum state by sampling a fitted Ising surrogate with a Trotterized quantum circuit, demonstrated on small spin and molecular systems.
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