SciPost Phys. Codebases 2 (2022) ·
published 23 August 2022
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· pdf
The introduction of Neural Quantum States (NQS) has recently given a new
twist to variational Monte Carlo (VMC). The ability to systematically reduce
the bias of the wave function ansatz renders the approach widely applicable.
However, performant implementations are crucial to reach the numerical state of
the art. Here, we present a Python codebase that supports arbitrary NQS
architectures and model Hamiltonians. Additionally leveraging automatic
differentiation, just-in-time compilation to accelerators, and distributed
computing, it is designed to facilitate the composition of efficient NQS
algorithms.
SciPost Phys. Codebases 2-r0.1 (2022) ·
published 23 August 2022
|
· src
The introduction of Neural Quantum States (NQS) has recently given a new
twist to variational Monte Carlo (VMC). The ability to systematically reduce
the bias of the wave function ansatz renders the approach widely applicable.
However, performant implementations are crucial to reach the numerical state of
the art. Here, we present a Python codebase that supports arbitrary NQS
architectures and model Hamiltonians. Additionally leveraging automatic
differentiation, just-in-time compilation to accelerators, and distributed
computing, it is designed to facilitate the composition of efficient NQS
algorithms.
SciPost Phys. 10, 147 (2021) ·
published 17 June 2021
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· pdf
Strongly interacting quantum systems described by non-stoquastic Hamiltonians
exhibit rich low-temperature physics. Yet, their study poses a formidable
challenge, even for state-of-the-art numerical techniques. Here, we investigate
systematically the performance of a class of universal variational
wave-functions based on artificial neural networks, by considering the
frustrated spin-$1/2$ $J_1-J_2$ Heisenberg model on the square lattice.
Focusing on neural network architectures without physics-informed input, we
argue in favor of using an ansatz consisting of two decoupled real-valued
networks, one for the amplitude and the other for the phase of the variational
wavefunction. By introducing concrete mitigation strategies against inherent
numerical instabilities in the stochastic reconfiguration algorithm we obtain a
variational energy comparable to that reported recently with neural networks
that incorporate knowledge about the physical system. Through a detailed
analysis of the individual components of the algorithm, we conclude that the
rugged nature of the energy landscape constitutes the major obstacle in finding
a satisfactory approximation to the ground state wavefunction, and prevents
learning the correct sign structure. In particular, we show that in the present
setup the neural network expressivity and Monte Carlo sampling are not primary
limiting factors.
SciPost Phys. 4, 013 (2018) ·
published 28 February 2018
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· pdf
The efficient representation of quantum many-body states with classical
resources is a key challenge in quantum many-body theory. In this work we
analytically construct classical networks for the description of the quantum
dynamics in transverse-field Ising models that can be solved efficiently using
Monte Carlo techniques. Our perturbative construction encodes time-evolved
quantum states of spin-1/2 systems in a network of classical spins with local
couplings and can be directly generalized to other spin systems and higher
spins. Using this construction we compute the transient dynamics in one, two,
and three dimensions including local observables, entanglement production, and
Loschmidt amplitudes using Monte Carlo algorithms and demonstrate the accuracy
of this approach by comparisons to exact results. We include a mapping to
equivalent artificial neural networks, which were recently introduced to
provide a universal structure for classical network wave functions.
Dr Schmitt: "Thank you for the thoughtful c..."
in Submissions | report on jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration