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Ground state search by local and sequential updates of neural network quantum states
Quantum Physics · Submitted 22 Jul 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We find that sequential local updates can lead to faster convergence to states which have energy and correlations closer to those of the ground state, depending on the size of the portion of the neural network which is locally updated.
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The abstract, sentence by sentence
Neural network quantum states are a promising tool to analyze complex quantum systems given their representative power. It can however be difficult to optimize efficiently and effectively the parameters of this type of ansatz. Here we propose a local optimization procedure which, when integrated with stochastic reconfiguration, outperforms previously used global optimization approaches. Specifically, we analyze both the ground state energy and the correlations for the non-integrable tilted Ising model with restricted Boltzmann machines. We find that sequential local updates can lead to faster convergence to states which have energy and correlations closer to those of the ground state, depending on the size of the portion of the neural network which is locally updated. To show the generality of the approach we apply it to both 1D and 2D non-integrable spin systems.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Neural network quantum states are a promising tool to analyze complex quantum systems given their representative power. | background 1.00 | 0.05 | 0.03 | asserted 0.72 |
| s1 | It can however be difficult to optimize efficiently and effectively the parameters of this type of ansatz. | background 0.67 | 0.06 | 0.02 | hedged possibility 0.52 |
| s2 | Here we propose a local optimization procedure which, when integrated with stochastic reconfiguration, outperforms previously used global optimization approaches. | contribution 0.86 | 0.83 | 0.70 | asserted 1.00 |
| s3 | Specifically, we analyze both the ground state energy and the correlations for the non-integrable tilted Ising model with restricted Boltzmann machines. | method 0.52 | 0.89 | 0.05 | asserted 0.86 |
| s4 | We find that sequential local updates can lead to faster convergence to states which have energy and correlations closer to those of the ground state, depending on the size of the portion of the neural network which is locally updated. | result 1.00 | 0.97 | 0.64 | asserted 0.91 |
| s5 | To show the generality of the approach we apply it to both 1D and 2D non-integrable spin systems. | contribution 0.72 | 0.92 | 0.07 | asserted 0.99 |
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The paper
Original abstract
Neural network quantum states are a promising tool to analyze complex quantum systems given their representative power. It can however be difficult to optimize efficiently and effectively the parameters of this type of ansatz. Here we propose a local optimization procedure which, when integrated with stochastic reconfiguration, outperforms previously used global optimization approaches. Specifically, we analyze both the ground state energy and the correlations for the non-integrable tilted Ising model with restricted Boltzmann machines. We find that sequential local updates can lead to faster convergence to states which have energy and correlations closer to those of the ground state, depending on the size of the portion of the neural network which is locally updated. To show the generality of the approach we apply it to both 1D and 2D non-integrable spin systems.
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How the literature uses it
2 arXiv papers cite this in 3 sentences
Papers citing it · 2 sentences without a confident label
Cites it as background 1 paper
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Neural network quantum states are a recently developed class of variational states [ref] that have shown great potential for parametrizing and finding the ground state of interacting quantum many-body systems [ref] .
Lee-Yang theory of quantum phase transitions with neural network quantum states · Introduction · 0.89
What it cites · 2 sentences without a confident label
Cites as background 6 papers
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Different strategies have been studied in order to improve the accuracy and speed of convergence of neural network quantum states algorithms, such as transfer learning [ref] , pruning [ref] , importance sampling [ref] , and massive parallelization [ref] .
$2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method · Introduction · 0.81
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Together with RBMs [ref] , other network structures such as a feed-forward [ref] , recurrent [ref] and convolutional neural networks [ref] have also been intensively studied.
Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks · Introduction · 0.98
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Together with RBMs [ref] , other network structures such as a feed-forward [ref] , recurrent [ref] and convolutional neural networks [ref] have also been intensively studied.
Expressive power of complex-valued restricted Boltzmann machines for solving non-stoquastic Hamiltonians · Introduction · 0.98
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