Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications | Paper Citation Record

Observation a6225453-0674-42b6-a8e5-66a292962f2c · outbound

This paper cites Carleo and M.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Carleo and M

Reference 1

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This paper cites Neural-network quantum states for many-body physics.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Neural-network quantum states for many-body physics

Reference 2

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This paper cites Cybenko, Mathematics of control, signals and systems2, 303 (1989).

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Cybenko, Mathematics of control, signals and systems2, 303 (1989)

Reference 3

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This paper cites Torlai, G.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Torlai, G

Reference 4

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Nagy and V.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Nagy and V

Reference 6

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Nomura, N.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Nomura, N

Reference 8

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This paper cites Carrasquilla, Physical Review Research2, 023358 (2020).

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Carrasquilla, Physical Review Research2, 023358 (2020)

Reference 9

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Robledo Moreno, G.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Robledo Moreno, G

Reference 12

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Donatella, Z.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Donatella, Z

Reference 16

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Chen and M.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Chen and M

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Le Roux and Y

Reference 20

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This paper cites Rrapaj and A.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Rrapaj and A

Reference 21

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This paper cites Sajjan, V.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Sajjan, V

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Vieijra, C

Reference 23

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Repesentation of general spin-$S$ systems using a Restricted Boltzmann Machine with Softmax Regression.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Repesentation of general spin-$S$ systems using a Restricted Boltzmann Machine with Softmax Regression

Reference 25

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Sajjan, H.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Sajjan, H

Reference 27

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This paper cites Sajjan, S.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Sajjan, S

Reference 28

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This paper cites Sajjan, J.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Sajjan, J

Reference 29

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications McBrian, G

Reference 32

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This paper cites Specialising Neural-network Quantum States for the Bose Hubbard Model.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Specialising Neural-network Quantum States for the Bose Hubbard Model

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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This paper cites Tensor-network-based variational Monte Carlo approach to the non-equilibrium steady state of open quantum systems.

Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Tensor-network-based variational Monte Carlo approach to the non-equilibrium steady state of open quantum systems

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications 10 (Springer, 2001)

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Neural-Network Quantum States: A Systematic Review

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Hermann, J

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Wolff, Physical Review Letters62, 361 (1989)

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Xia and S

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Cerezo, A

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Xia and S

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications DeCross, E

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Xu and Y

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Kanno, H

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Mazzola, The Journal of Chemical Physics160 (2024)

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Babbush, and J

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Quantum Shadow Gradient Descent for Variational Quantum Algorithms

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Does provable absence of barren plateaus imply classical simulability?

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Smolenskyet al., (1986)

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Salakhutdinov, A

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Nagy and V

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Borin and D

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Neural network approach for non-markovian dissipative dynamics of many-body open quantum systems,

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Hagai, M

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Pan and C

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Borin and D

Reference 78

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Reference 81

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Chib and E

Reference 82

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Derflinger, W

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Mezera, J

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

Reference 86

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Zyczkowski and H.-J

Reference 87

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Alagic, C

Reference 88

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Kim, and S

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

Reference 90

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Bhatnagar, A

Reference 91

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

Reference 92

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Yi and E

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

Reference 94

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

Reference 95

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Accurate quantum-centric simulations of supramolecular interactions

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Bethe, Zeitschrift für Physik71, 205 (1931)

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Unresolved cited work

Reference 98

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Scheie, N

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Ljubotina, M

Reference 100

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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications Dupont, N

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