Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians | Paper Citation Record

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This paper cites This implies that for lower values of g, HANNNI should be a hard model for MCMC-based VQMC methods compared to the exact sampling method.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians This implies that for lower values of g, HANNNI should be a hard model for MCMC-based VQMC methods compared to the exact sampling method

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This paper cites In our benchmarks, we adopt the Quimb li- brary [16] and the ITensor library [15] as reference im- plementations of tensor network algorithms.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians In our benchmarks, we adopt the Quimb li- brary [16] and the ITensor library [15] as reference im- plementations of tensor network algorithms

Reference 2

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This paper cites Instead of defining the quantum state from a parameterized family of prob- ability distributions, now we define a ”Neural Quantum State” [5] (NQS), as the variational Ansatz.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Instead of defining the quantum state from a parameterized family of prob- ability distributions, now we define a ”Neural Quantum State” [5] (NQS), as the variational Ansatz

Reference 3

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This paper cites Then the gradient could be estimated efficiently as in (6) with the help of automatic differentiation.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Then the gradient could be estimated efficiently as in (6) with the help of automatic differentiation

Reference 4

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This paper cites Solving the quan- tum many-body problem with artificial neural networks.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Solving the quan- tum many-body problem with artificial neural networks

Reference 5

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This paper cites Deep learning of representations for un- supervised and transfer learning.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Deep learning of representations for un- supervised and transfer learning

Reference 6

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This paper cites Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures

Reference 7

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This paper cites Gauge-invariant method for the±j spin-glass model.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Gauge-invariant method for the±j spin-glass model

Reference 8

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This paper cites Monte Carlo simulation of stoquastic Hamiltonians.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Monte Carlo simulation of stoquastic Hamiltonians

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This paper cites Two- dimensional frustrated j 1- j 2 model studied with neural network quantum states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Two- dimensional frustrated j 1- j 2 model studied with neural network quantum states

Reference 10

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This paper cites Monte carlo simulation of a many-fermion study.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Monte carlo simulation of a many-fermion study

Reference 11

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This paper cites Critical behavior of the ising spin- glass models in a transverse field.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Critical behavior of the ising spin- glass models in a transverse field

Reference 12

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This paper cites Symmetries and many-body excitations with neural-network quantum states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Symmetries and many-body excitations with neural-network quantum states

Reference 13

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This paper cites Fermionic neural-network states for ab-initio electronic structure.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Fermionic neural-network states for ab-initio electronic structure

Reference 14

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This paper cites White, and E.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians White, and E

Reference 15

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This paper cites Renormaliza- tion and tensor product states in spin chains and lat- tices.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Renormaliza- tion and tensor product states in spin chains and lat- tices

Reference 16

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This paper cites Markov random fields in statistics.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Markov random fields in statistics

Reference 17

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This paper cites Quan- tum entanglement in neural network states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Quan- tum entanglement in neural network states

Reference 18

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This paper cites Transverse ising spin-glass model.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Transverse ising spin-glass model

Reference 19

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This paper cites Learning energy-based representations of quan- tum many-body states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Learning energy-based representations of quan- tum many-body states

Reference 20

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This paper cites quimb: a python library for quantum in- formation and many-body calculations.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians quimb: a python library for quantum in- formation and many-body calculations

Reference 21

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This paper cites Learning of discrete graphical models with neu- ral networks.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Learning of discrete graphical models with neu- ral networks

Reference 22

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This paper cites Many-body problem with strong forces.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Many-body problem with strong forces

Reference 23

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This paper cites Discrete distributions are learnable from metastable samples.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Discrete distributions are learnable from metastable samples

Reference 24

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This paper cites From architectures to applica- tions: A review of neural quantum states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians From architectures to applica- tions: A review of neural quantum states

Reference 25

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This paper cites Ac- curate determination of tensor network state of quantum lattice models in two dimensions.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Ac- curate determination of tensor network state of quantum lattice models in two dimensions

Reference 26

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This paper cites Classical simulation of infinite-size quantum lattice systems in two spatial di- mensions.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Classical simulation of infinite-size quantum lattice systems in two spatial di- mensions

Reference 27

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This paper cites Kingma and Jimmy Ba.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Kingma and Jimmy Ba

Reference 28

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This paper cites Statistical mechanics: algorithms and computations, volume 13.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Statistical mechanics: algorithms and computations, volume 13

Reference 29

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This paper cites Symmetry- projected jastrow mean-field wave function in variational monte carlo.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Symmetry- projected jastrow mean-field wave function in variational monte carlo

Reference 30

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This paper cites Hybrid con- volutional neural network and projected entangled pair states wave functions for quantum many-particle states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Hybrid con- volutional neural network and projected entangled pair states wave functions for quantum many-particle states

Reference 31

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This paper cites Optimal structure and param- eter learning of Ising models.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Optimal structure and param- eter learning of Ising models

Reference 32

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This paper cites Algorithms for finite projected entangled pair states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Algorithms for finite projected entangled pair states

Reference 33

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This paper cites Lack of ergodic- ity in the infinite-range ising spin-glass.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Lack of ergodic- ity in the infinite-range ising spin-glass

Reference 34

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This paper cites Efficient 2d tensor network simu- lation of quantum systems.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Efficient 2d tensor network simu- lation of quantum systems

Reference 35

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This paper cites Ground state of liquid he 4.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Ground state of liquid he 4

Reference 36

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This paper cites Information, physics, and computation.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Information, physics, and computation

Reference 37

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This paper cites A practical introduction to tensor net- works: Matrix product states and projected entangled pair states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians A practical introduction to tensor net- works: Matrix product states and projected entangled pair states

Reference 38

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This paper cites Thermodynamic limit of density matrix renormalization.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Thermodynamic limit of density matrix renormalization

Reference 39

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This paper cites Two-dimensional ising mod- els with competing interaction—a monte carlo study.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Two-dimensional ising mod- els with competing interaction—a monte carlo study

Reference 40

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This paper cites One-dimensional ising chain with competing interactions: Exact results and connection with other sta- tistical models.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians One-dimensional ising chain with competing interactions: Exact results and connection with other sta- tistical models

Reference 41

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This paper cites Group Convolutional Neural Networks Improve Quantum State Accuracy.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Group Convolutional Neural Networks Improve Quantum State Accuracy

Reference 42

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Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians The 2d ±j ising spin glass: exact partition functions in polynomial time

Reference 43

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This paper cites The density-matrix renormalization group in the age of matrix product states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians The density-matrix renormalization group in the age of matrix product states

Reference 44

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This paper cites NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

Reference 45

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This paper cites Finite-size behaviour of the two- dimensional annni model.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Finite-size behaviour of the two- dimensional annni model

Reference 46

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This paper cites Deep autoregressive models for the efficient variational simulation of many-body quantum systems.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Deep autoregressive models for the efficient variational simulation of many-body quantum systems

Reference 47

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This paper cites Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions

Reference 48

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This paper cites Matrix product states, projected entangled pair states, and variational renormalization group methods for quan- tum spin systems.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Matrix product states, projected entangled pair states, and variational renormalization group methods for quan- tum spin systems

Reference 49

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Observation 3958fe1e-2a17-4b4c-ad00-bfa27d4a7f2b · outbound

This paper cites Density-matrix algorithms for quantum renormalization groups.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Density-matrix algorithms for quantum renormalization groups

Reference 50

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This paper cites On the other hand, for NNQS models the SR preconditioning gives an advantage over first-order methods [45].

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians On the other hand, for NNQS models the SR preconditioning gives an advantage over first-order methods [45]

Reference 51

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Observation ec6a6b56-46d5-4601-9cff-4acf86e3f131 · outbound

This paper cites Restricted boltzmann machines for quantum states with non-abelian or anyonic symmetries.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Restricted boltzmann machines for quantum states with non-abelian or anyonic symmetries

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:54.720342Z

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Observation 8ef09cfc-bac8-46d8-9bed-a5698c4ebd11 · outbound

This paper cites Effi- cient learning of discrete graphical models.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Effi- cient learning of discrete graphical models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:54.707789Z

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Observation 02f08f1a-c82f-4607-9b6a-231ff0d575f0 · outbound

This paper cites Interaction screening: Efficient and sample-optimal learning of Ising models.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Interaction screening: Efficient and sample-optimal learning of Ising models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:54.694867Z

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Observation 5ae2f858-dc6f-4dd6-b093-1dc62cf8f374 · outbound

This paper cites Generalization properties of neural network approxima- tions to frustrated magnet ground states.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Generalization properties of neural network approxima- tions to frustrated magnet ground states

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:54.683201Z

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Observation dfb5b46b-c3f7-4d2d-bb00-967078aba518 · outbound

This paper cites From Tensor Network Quantum States to Tensorial Recurrent Neural Networks.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians From Tensor Network Quantum States to Tensorial Recurrent Neural Networks

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:37:54.571002Z

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Observation f25396d5-2390-4a60-a9d7-25205ca616a5 · outbound

This paper cites Solving statisti- cal mechanics using variational autoregressive networks.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians Solving statisti- cal mechanics using variational autoregressive networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:54.658517Z

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Observation 7765ecfa-c1ec-4de3-af2a-dc54ba5de225 · outbound

This paper cites How transferable are features in deep neural net- works? Advances in neural information processing sys- tems, 27, 2014.

Autoregressive pairwise Graphical Models efficiently find ground state representations of stoquastic Hamiltonians How transferable are features in deep neural net- works? Advances in neural information processing sys- tems, 27, 2014

Reference 58

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:37:54.646488Z

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